CN115795276A - Secondary circuit pressing plate state evaluation method based on wavelet analysis and machine learning - Google Patents
Secondary circuit pressing plate state evaluation method based on wavelet analysis and machine learning Download PDFInfo
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Abstract
The invention discloses a method for evaluating the state of a secondary circuit pressing plate based on wavelet analysis and machine learning, which comprises the following steps: the method comprises the following steps: extracting secondary circuit pressing plate state characteristic quantity based on wavelet analysis, namely analyzing pressing plate state data with background noise, and extracting characteristic quantity capable of distinguishing the secondary circuit pressing plate state; step two: and (3) evaluating the state of the secondary circuit pressing plate based on machine learning, namely, establishing a state evaluation model based on historical data of the secondary circuit pressing plate through learning and training of the machine on the state characteristic quantity of the secondary circuit pressing plate. According to the invention, the wavelet analysis method is utilized to optimize the state evaluation data of the secondary circuit pressing plate, so that a result with both reliability and accuracy is obtained, and meanwhile, system risks such as false operation, malfunction, false opening and the like of a secondary protection device are avoided; the method can still obtain a good judgment result under the condition of containing a large amount of data noise, reduces the requirement on hardware and has good robustness.
Description
Technical Field
The invention relates to the technical field of evaluation of a secondary circuit pressing plate of a transformer substation, in particular to a method for evaluating the state of the secondary circuit pressing plate based on wavelet analysis and machine learning.
Background
The secondary circuit pressing plate is important equipment in a secondary circuit of a transformer substation relay protection device and an automatic device, and comprises a functional pressing plate and an outlet pressing plate according to different circuit positions of an access protection device and the automatic device. The states of the pressing plates comprise an input state and an exit state, and the combination of all the pressing plates of each set of device can realize the conversion among the four states of operation, maintenance, exit and abnormity of the relay protection device. The states of the protection tripping pressure plate and the opening-in opening pressure plate mainly comprise the opening and the exiting, when the outlet contact of the protection device is not separated or the outlet contact is not restored, or the external loop of the secondary loop is in short circuit, the conditions of switching operation, not measuring the voltages at two ends of the pressure plate and the like are added, and if the pressure plate is closed, the system risks of misoperation, failure, opening by mistake and the like of the secondary protection device can be caused, so the state of the pressure plate is very important for the reliability of the operation of the pressure plate.
Although state evaluation and state overhaul of the secondary circuit pressing plate are carried out at present, the judgment of the pressing plate separation and combination point is the premise of the state evaluation of the pressing plate, a large amount of research is carried out on the pressing plate at home and abroad, and a lot of effective achievements are obtained. In order to solve the two problems of misoperation of the protection pressing plate and large patrol workload, researchers draw switching value information from the pressing plate and obtain the switching state of the pressing plate by detecting the switching value information of the pressing plate, such as a mutation point judgment method based on a speed curve, a vibration signal analysis method by utilizing a wavelet packet and a short-time energy method, and the like, and the methods have proved to have good effects. However, in specific applications, there are some problems: firstly, utilize state characteristic quantity signal to judge the clamp plate point of separation and reunion, must increase three routes signal channel, put forward higher requirement to hardware circuit, the price/performance ratio is not high, secondly on-the-spot environmental noise disturbs seriously, and state characteristic quantity signal is difficult to distinguish. Therefore, researching a state evaluation algorithm with high efficiency, high precision and low cost suitable for the secondary circuit pressing plate in the complex environment of the transformer substation becomes important content of online monitoring of secondary equipment.
Disclosure of Invention
The invention aims to provide a secondary circuit pressing plate state evaluation method based on wavelet analysis and machine learning, and high-reliability and high-precision state evaluation of a secondary circuit pressing plate of an intelligent substation is realized through secondary circuit pressing plate state characteristic quantity extraction and secondary circuit pressing plate state evaluation.
The invention provides a secondary circuit pressing plate state evaluation method based on wavelet analysis and machine learning, which is characterized by comprising the following steps of: the method comprises the following steps:
the method comprises the following steps: extracting secondary circuit pressing plate state characteristic quantity based on wavelet analysis, namely analyzing pressing plate state data with background noise, and extracting characteristic quantity capable of distinguishing the secondary circuit pressing plate state;
step two: and (3) evaluating the state of the secondary circuit pressing plate based on machine learning, namely, establishing a state evaluation model based on historical data of the secondary circuit pressing plate through learning and training of the machine on the state characteristic quantity of the secondary circuit pressing plate.
Further, the step one specifically includes the following steps:
s11: performing 5-layer decomposition on the acquired characteristic quantity data of the original state of the secondary circuit pressing plate based on a wavelet analysis method, and performing threshold quantization on a wavelet decomposition high-frequency coefficient by adopting a heuristic threshold;
s12: because the wavelet coefficient value of the signal is generally larger than that of the noise, the high-frequency coefficient obtained by decomposition and quantization processing is screened and the one-dimensional signal is reconstructed, so that the noise is eliminated, the characteristic quantity data of the effective secondary circuit pressing plate is reserved, and a denoised secondary circuit pressing plate characteristic quantity data set is formed;
s13: continuously performing 5-layer wavelet decomposition on the denoised secondary circuit pressing plate characteristic quantity data set in the step S12, and performing signal reconstruction on a first layer to form a reconstructed secondary circuit pressing plate state characteristic quantity data set so as to detect a signal mutation point;
s14: based on the reconstructed secondary circuit pressing plate state characteristic quantity data set, signal envelopes of the secondary circuit pressing plate state characteristic quantity data set are extracted through Hilbert transformation, and abrupt change information of signals can be obtained through analysis of signal envelope waveforms, so that the secondary circuit pressing plate state characteristic quantity is obtained.
Further, the second step specifically includes the following steps:
s21: dividing a historical state data set of a secondary circuit pressing plate of an intelligent substation into machine training data and verification test data according to a certain proportion;
s22: normalizing the data of the state characteristic quantities of the secondary circuit pressing plate, classifying the characteristic quantity training data under different scenes, and obtaining a state characteristic quantity set under different operation scenes by adopting a clustering method;
s23: bringing the state characteristic quantity of the training data of the same category in the step S22 into a machine learning evaluation model, randomly giving the input weight of the machine learning evaluation model, taking the on-off state of a secondary circuit pressing plate corresponding to the training data as the initial output value of the evaluation model, and training the model to obtain the initial output weight;
s24: according to the steps of a genetic algorithm, optimizing the initial output weight in the step S23, bringing the secondary circuit pressing plate switching state value and the state characteristic quantity of the verification test data into the initial output weight, calculating the adaptive value of each individual in each iteration according to a comprehensive optimization index, and searching a global optimal solution to obtain the optimal output weight;
s25: and (3) taking the accurate value of the state characteristic quantity of the secondary circuit pressing plate into the measured data, and obtaining a final output weight, namely a state evaluation result of the secondary circuit pressing plate, by adopting an evaluation model after parameter optimization.
Further, performing envelope calculation on the denoised secondary circuit pressing plate characteristic quantity data set to obtain an accurate numerical value of the secondary circuit pressing plate characteristic quantity, wherein the envelope calculation method comprises the following steps:
wherein x (t) is the characteristic quantity data of the state of the secondary circuit pressing plate after noise elimination,
is its Hilbert transform value; taking x (t) as a real part,
analyzing data for an imaginary part structure, and calculating envelope values A of the characteristic quantity as follows:
a (t) is the signal envelope curve of the secondary circuit pressing plate state characteristic quantity data x (t).
Further, a neural network model is utilized to establish a single hidden layer extreme learning machine press plate state evaluation model with L hidden layer nodes:
in the formula, g (x) is an activation function, a hyperbolic tangent Sigmoid function is adopted as the activation function, wi is an input weight, beta i is an output weight, bi is the bias of the ith hidden layer unit, xj is a characteristic quantity value of the state of the secondary circuit pressing plate, yj is the state of the secondary circuit pressing plate, namely the on-off state, and N is the total number of training samples;
the matrix can be expressed as: h β = Y:
in the formula, H represents a hidden node output, and Y is a desired output.
Further, the secondary circuit pressing plate state characteristic quantity comprises pressing plate displacement, an inclination angle and acceleration.
Has the beneficial effects that: compared with the prior art, the method has the advantages that the wavelet analysis method is utilized to optimize the state evaluation data of the secondary circuit pressing plate, the result of considering both reliability and accuracy is obtained, and meanwhile, system risks such as false operation, failure and false opening of a secondary protection device are avoided; the method can still obtain a good judgment result under the condition of containing a large amount of data noise, reduces the requirement on hardware and has good robustness.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an evaluation method according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in the figure, the method for evaluating the state of the secondary circuit pressing plate based on wavelet analysis and machine learning is suitable for evaluating the state of the secondary circuit pressing plate of the intelligent substation. The secondary circuit pressure plate state evaluation flow chart provided by the invention is shown in figure 1, and the method mainly comprises two parts: extracting the characteristic quantity of the state of the secondary circuit pressing plate based on wavelet analysis and evaluating the state of the secondary circuit pressing plate based on machine learning.
The wavelet analysis-based secondary circuit pressing plate state characteristic quantity extraction is used for analyzing pressing plate state data with background noise, and extracting characteristic quantities capable of distinguishing the secondary circuit pressing plate state so as to provide a reliable data basis for secondary circuit pressing plate state evaluation; the secondary circuit pressing plate state evaluation based on machine learning is trained through machine learning, and a state evaluation model based on historical data of the secondary circuit pressing plate is established so as to solve the problem of secondary circuit pressing plate state identification.
The method for extracting the secondary circuit pressing plate state characteristic quantity based on wavelet analysis analyzes the pressing plate state data with background noise, extracts the characteristic quantity capable of distinguishing the pressing plate state, and provides a reliable data base for secondary circuit pressing plate state evaluation, wherein the extraction of the secondary circuit pressing plate state characteristic quantity comprises the following steps:
s11: performing 5-layer decomposition on the acquired characteristic quantity data of the original state of the secondary circuit pressing plate based on a wavelet analysis method, and performing threshold quantization on a wavelet decomposition high-frequency coefficient by adopting a heuristic threshold;
s12: because the wavelet coefficient value of the signal is generally larger than that of the noise, the high-frequency coefficient obtained by decomposition and quantization processing is screened and the one-dimensional signal is reconstructed, so that the noise is eliminated, the characteristic quantity data of the effective secondary circuit pressing plate is reserved, and a denoised secondary circuit pressing plate characteristic quantity data set is formed;
s13: continuously performing 5-layer wavelet decomposition on the denoised secondary circuit pressing plate characteristic quantity data set in the step S12, and performing signal reconstruction on a first layer to form a reconstructed secondary circuit pressing plate state characteristic quantity data set so as to detect a signal mutation point;
s14: based on the reconstructed secondary circuit pressing plate state characteristic quantity data, signal envelopes of the secondary circuit pressing plate state characteristic quantity data are extracted through Hilbert transformation, and abrupt change information of signals can be obtained through analysis of signal envelope waveforms, so that accurate numerical values of characteristic quantities such as pressing plate displacement, inclination angles and acceleration are obtained.
The secondary circuit pressing plate state evaluation based on machine learning is trained through machine learning, a state evaluation model based on historical data of the secondary circuit pressing plate is established, and therefore the problem of secondary circuit pressing plate state identification is solved, and the specific evaluation process is as follows:
s21: dividing a historical state data set of a secondary equipment loop pressing plate of an intelligent substation into machine training data and verification test data according to a certain proportion;
s22: normalizing the state characteristic quantity (including displacement, inclination angle, acceleration and the like) data of the secondary circuit pressing plate, classifying the characteristic quantity training data under different scenes, and obtaining a state characteristic quantity set under different operation scenes by adopting a clustering method;
s23: bringing the state characteristic quantity of the training data of the same category in the step S22 into a machine learning evaluation model, randomly giving the input weight of the machine learning evaluation model, taking the on-off state of a secondary circuit pressing plate corresponding to the training data as the initial output value of the evaluation model, and training the model to obtain the initial output weight;
s24: according to the steps of a genetic algorithm, optimizing the initial output weight in the step S23, bringing the secondary circuit pressing plate switching state value and the state characteristic quantity of the verification test data into the initial output weight, calculating the adaptive value of each individual in each iteration according to a comprehensive optimization index, and searching a global optimal solution to obtain the optimal output weight;
s25: and (3) taking the accurate value of the state characteristic quantity of the secondary circuit pressing plate into the measured data, and obtaining a final output weight, namely a state evaluation result of the secondary circuit pressing plate, by adopting an evaluation model after parameter optimization.
Performing envelope calculation on the denoised state characteristic quantity data by using Hilbert transformation to obtain accurate numerical values of characteristic quantities such as displacement, inclination angle and acceleration of the pressure plate, wherein the envelope calculation method comprises the following steps:
wherein x (t) is the characteristic quantity data of the state of the secondary circuit pressing plate after noise elimination,
is its Hilbert transformed value. Taking x (t) as a real part,
analyzing data for an imaginary part structure, and calculating envelope values A of the characteristic quantity as follows:
a (t) is the signal envelope curve of the secondary circuit pressing plate state characteristic quantity data x (t).
Establishing a single-hidden-layer extreme learning machine pressure plate state evaluation model with L hidden-layer nodes by using a neural network model:
in the formula, g (x) is an activation function, a hyperbolic tangent Sigmoid function is adopted as the activation function, wi is an input weight, beta i is an output weight, bi is the bias of the ith hidden layer unit, xj is a characteristic quantity value of a secondary circuit pressing plate state, yj is a secondary circuit pressing plate state, namely a throwing-withdrawing state, and N is the total number of training samples;
the matrix can be expressed as: h β = Y:
in the formula, H represents a hidden node output, and Y is a desired output.
In the above embodiments, all functions may be implemented, or a part of the functions may be implemented as necessary.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A secondary circuit pressing plate state evaluation method based on wavelet analysis and machine learning is characterized in that: the method comprises the following steps:
the method comprises the following steps: extracting secondary circuit pressing plate state characteristic quantity based on wavelet analysis, namely analyzing pressing plate state data with background noise, and extracting characteristic quantity capable of distinguishing the secondary circuit pressing plate state;
step two: and (3) evaluating the state of the secondary circuit pressing plate based on machine learning, namely, establishing a state evaluation model based on historical data of the secondary circuit pressing plate through learning and training of the machine on the state characteristic quantity of the secondary circuit pressing plate.
2. The wavelet analysis and machine learning-based secondary loop platen state assessment method according to claim 1, wherein: the first step specifically comprises the following steps:
s11: performing 5-layer decomposition on the acquired characteristic quantity data of the original state of the secondary circuit pressing plate based on a wavelet analysis method, and performing threshold quantization on a wavelet decomposition high-frequency coefficient by adopting a heuristic threshold;
s12: because the wavelet coefficient value of the signal is generally greater than that of the noise, the high-frequency coefficient obtained by decomposition and quantization processing is screened and the one-dimensional signal is reconstructed, so that the noise is eliminated, the characteristic quantity data of the effective secondary circuit pressing plate is reserved, and a denoised characteristic quantity data set of the secondary circuit pressing plate is formed;
s13: continuously performing 5-layer wavelet decomposition on the denoised secondary circuit pressing plate characteristic quantity data set in the step S12, and performing signal reconstruction on a first layer to form a reconstructed secondary circuit pressing plate state characteristic quantity data set so as to detect a signal mutation point;
s14: based on the reconstructed secondary circuit pressing plate state characteristic quantity data set, signal envelopes of the secondary circuit pressing plate state characteristic quantity data set are extracted through Hilbert transformation, and abrupt change information of signals can be obtained through analysis of signal envelope waveforms, so that the secondary circuit pressing plate state characteristic quantity is obtained.
3. The wavelet analysis and machine learning-based secondary loop platen state assessment method according to claim 1, wherein: the second step specifically comprises the following steps:
s21: dividing a historical state data set of a secondary circuit pressing plate of an intelligent substation into machine training data and verification test data according to a certain proportion;
s22: normalizing the data of the state characteristic quantity of the secondary circuit pressing plate, classifying the characteristic quantity training data under different scenes, and obtaining a state characteristic quantity set under different operation scenes by adopting a clustering method;
s23: substituting the training data state characteristic quantity of the same type in the step S22 into a machine learning evaluation model, randomly giving the input weight of the machine learning evaluation model, taking the on-off state of a secondary circuit pressing plate corresponding to the training data as the initial output value of the evaluation model, and training the model to obtain the initial output weight;
s24: according to the steps of a genetic algorithm, optimizing the initial output weight in the step S23, bringing the secondary circuit pressing plate switching state value and the state characteristic quantity of the verification test data into the initial output weight, calculating the adaptive value of each individual in each iteration according to a comprehensive optimization index, and searching a global optimal solution to obtain the optimal output weight;
s25: and (3) taking the accurate value of the state characteristic quantity of the secondary circuit pressing plate into the measured data, and obtaining a final output weight, namely a state evaluation result of the secondary circuit pressing plate, by adopting an evaluation model after parameter optimization.
4. The wavelet analysis and machine learning-based secondary loop platen state assessment method according to claim 2, wherein: performing envelope calculation on the denoised secondary circuit pressing plate characteristic quantity data set to obtain an accurate numerical value of the secondary circuit pressing plate characteristic quantity, wherein the envelope calculation method comprises the following steps:
wherein x (t) is the characteristic quantity data of the state of the secondary circuit pressing plate after noise elimination,
is its Hilbert transform value; taking x (t) as a real part,
analyzing data for an imaginary part structure, and calculating envelope values A of the characteristic quantity as follows:
a (t) is the signal envelope curve of the secondary circuit pressing plate state characteristic quantity data x (t).
5. The wavelet analysis and machine learning-based secondary loop platen state assessment method according to claim 3, wherein: establishing a single-hidden-layer extreme learning machine pressure plate state evaluation model with L hidden-layer nodes by using a neural network model:
in the formula, g (x) is an activation function, a hyperbolic tangent Sigmoid function is adopted as the activation function, wi is an input weight, beta i is an output weight, bi is the bias of the ith hidden layer unit, xj is a characteristic quantity value of the state of the secondary circuit pressing plate, yj is the state of the secondary circuit pressing plate, namely the on-off state, and N is the total number of training samples;
the matrix can be expressed as: h β = Y:
in the formula, H represents a hidden node output, and Y is a desired output.
6. The wavelet analysis and machine learning based secondary loop platen state assessment method according to any one of claims 1-5, wherein: the secondary circuit pressing plate state characteristic quantity comprises pressing plate displacement, an inclination angle and acceleration.
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