CN113987909A - Oil paper insulation aging prediction method and device, computer equipment and storage medium - Google Patents
Oil paper insulation aging prediction method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a prediction method and device for oiled paper insulation aging, computer equipment and a storage medium. The method comprises the steps of obtaining an aging characteristic parameter to be predicted corresponding to the oil paper insulation to be predicted, inputting the aging characteristic parameter to be predicted into a target aging prediction model obtained by training a genetic partial least square method, sample aging characteristic parameters of a plurality of sample oil paper insulations and a sample real aging parameter, obtaining a predicted aging parameter output by the target aging prediction model, and determining a predicted aging stage corresponding to the oil paper insulation to be predicted according to the predicted aging parameter. Compared with the traditional manual observation of the aging degree of the oiled paper insulation. According to the scheme, the target aging prediction model obtained based on the genetic partial least square method training is used for predicting the aging stage of the oil paper insulation according to the aging characteristic of the oil paper insulation, so that the prediction accuracy is improved.
Description
Technical Field
The application relates to the technical field of electric power, in particular to a prediction method and device for insulation aging of oiled paper, computer equipment and a storage medium.
Background
The transformer bears the important task of energy conversion in the process of electric energy transmission and distribution in the power grid, and the safe operation of the transformer is the core for ensuring the safety and stability of the power system. The oil paper insulation can be installed in the oil-immersed transformer, so that the safety of the oil-immersed transformer in the operation can be ensured. However, in the long-term operation process of the oil-immersed transformer, the oil-paper insulation is affected by multiple factors such as electricity, heat and environment, and the electrical and mechanical properties of the oil-immersed transformer are reduced. Therefore, the method can accurately diagnose the oil paper insulation aging state of the oil-immersed electrical equipment, prevent the oil paper insulation aging state in the bud and is an important technical support for ensuring the safe and reliable operation of the large-scale oil-immersed electrical equipment. The current aging prediction for paper-oil insulation is usually observed manually. However, the way of observation by human is liable to make the prediction result inaccurate.
Therefore, the current prediction method for the insulation aging of the oil paper has the defect of low prediction accuracy.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device, and a storage medium for predicting degradation of oiled paper insulation, which can improve the prediction accuracy.
A method of predicting the aging of a paper oil insulation, the method comprising:
acquiring aging characteristic parameters to be predicted corresponding to the insulation of the oil paper to be predicted;
inputting the aging characteristic parameters to be predicted into a target aging prediction model, and acquiring predicted aging parameters output by the target aging prediction model; the target aging prediction model is obtained based on a genetic partial least square method and training of sample aging characteristic parameters and sample real aging parameters of a plurality of sample oil paper insulators;
and determining a prediction aging stage corresponding to the insulation of the oil paper to be predicted according to the prediction aging parameters.
In one embodiment, the method further comprises:
obtaining a plurality of sample aging characteristic parameters corresponding to a plurality of sample oil paper insulations and obtaining a plurality of sample real aging parameters corresponding to the plurality of sample oil paper insulations;
acquiring an aging prediction model to be trained; the aging prediction model to be trained comprises an input layer, a hidden layer and an output layer; each layer in the aging prediction model to be trained comprises a corresponding threshold value, and each layer comprises a corresponding weight value;
inputting the plurality of sample aging characteristic parameters into the input layer, and acquiring a plurality of sample prediction aging parameters output by the aging prediction model to be trained based on the hidden layer and the output layer;
obtaining an error value of each sample predicted aging parameter and a corresponding sample real aging parameter, and judging whether the error value is smaller than a preset error threshold value or not;
if not, respectively adjusting weights among all layers and thresholds of all layers in the aging prediction model to be trained according to the error value, and returning to the step of inputting the aging characteristic parameters of the multiple samples into the input layer;
if so, ending circulation, and obtaining the target aging prediction model according to the weight values among all layers and the threshold values of all layers in the aging prediction model to be trained when the current training is ended.
In one embodiment, the obtaining of the plurality of sample true aging parameters corresponding to the plurality of sample oiled paper insulations includes:
aiming at each sample oilpaper insulation, obtaining a Raman spectrogram of the sample oilpaper insulation;
and removing a spectrum base line, removing a peak and reducing noise of the Raman spectrum to obtain a target Raman spectrum corresponding to the sample oiled paper insulation, wherein the target Raman spectrum is used as a real aging parameter of the sample.
In one embodiment, the inputting the plurality of sample aging characteristic parameters into the input layer, and obtaining a plurality of sample predicted aging parameters output by the aging prediction model to be trained based on the hidden layer and the output layer includes:
acquiring a first weight corresponding to a first node in the input layer and a second node in the hidden layer, and a first threshold of the second node;
acquiring a second weight corresponding to a second node in the hidden layer and a third node in the output layer, and a second threshold of the third node;
inputting the sample aging characteristic parameters into the input layer aiming at each sample aging characteristic parameter, and acquiring a first output result of the sample aging characteristic parameters in the hidden layer according to the first weight, the first threshold and a preset activation function;
and obtaining a second output result of the sample aging characteristic parameter on the output layer according to the second weight, the second threshold, the first output result and the preset activation function, and obtaining a sample predicted aging parameter corresponding to the sample aging characteristic parameter.
In one embodiment, obtaining an error value between each sample predicted aging parameter and the corresponding sample real aging parameter comprises:
obtaining a root mean square error value of the sample predicted aging parameter and a corresponding real aging parameter;
obtaining a corresponding prediction set correlation coefficient according to the square of the difference between the sample predicted aging parameter and the sample real aging parameter and the square of the difference between the sample real aging parameter and the average value of the sample real aging parameters;
and taking the root mean square error value and the prediction set correlation coefficient as the error value.
In one embodiment, the adjusting the weights between layers and the thresholds of the layers in the aging prediction model to be trained according to the error value includes:
acquiring a corresponding error function according to the sample predicted aging parameter and the sample real aging parameter;
and adjusting the first weight, the first threshold, the second weight and the second threshold based on a Widrop-Hoff learning rule and a momentum gradient descent algorithm according to the error function and a preset activation function.
In one embodiment, the predicted aging parameter is a predicted raman spectrogram corresponding to the aging characteristic parameter to be predicted;
and determining a prediction aging stage corresponding to the oil paper insulation to be predicted according to the prediction aging parameter, wherein the prediction aging stage comprises the following steps:
inquiring an aging stage table according to the prediction Raman spectrogram, and determining a prediction aging stage corresponding to the oil paper insulation to be predicted; the aging stage table comprises the corresponding relation between a plurality of aging stages of the oilpaper insulation and the Raman spectrogram.
An apparatus for predicting degradation of oiled paper insulation, the apparatus comprising:
the acquisition module is used for acquiring the aging characteristic parameters to be predicted corresponding to the insulation of the oil paper to be predicted;
the input module is used for inputting the aging characteristic parameters to be predicted into a target aging prediction model and acquiring predicted aging parameters output by the target aging prediction model; the target aging prediction model is obtained based on a genetic partial least square method and training of sample aging characteristic parameters and sample real aging parameters of a plurality of sample oil paper insulators;
and the prediction module is used for determining a prediction aging stage corresponding to the oil paper insulation to be predicted according to the prediction aging parameter.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
According to the oil paper insulation aging prediction method, the oil paper insulation aging prediction device, the computer equipment and the storage medium, the aging characteristic parameter to be predicted corresponding to the oil paper insulation to be predicted is obtained, the aging characteristic parameter to be predicted is input into a target aging prediction model obtained based on a genetic partial least square method, sample aging characteristic parameters of a plurality of sample oil paper insulations and real aging parameters of the samples through training, the aging prediction parameter output by the target aging prediction model is obtained, and therefore the aging prediction stage corresponding to the oil paper insulation to be predicted is determined according to the aging prediction parameter. Compared with the traditional manual observation of the aging degree of the oiled paper insulation. According to the scheme, the target aging prediction model obtained based on the genetic partial least square method training is used for predicting the aging stage of the oil paper insulation according to the aging characteristic of the oil paper insulation, so that the prediction accuracy is improved.
Drawings
FIG. 1 is a diagram of an application environment of a prediction method for the aging of the insulation of the oiled paper in one embodiment;
FIG. 2 is a schematic flow chart of a method for predicting the aging of the oiled paper insulation in one embodiment;
FIG. 3 is a block diagram of an aging prediction model in one embodiment;
FIG. 4 is a schematic flow chart of the genetic partial least squares method in one embodiment;
FIG. 5 is a block diagram showing the structure of an apparatus for predicting the deterioration of insulation of oiled paper in one embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The prediction method for the insulation aging of the oil paper can be applied to the application environment shown in fig. 1. The terminal 102 can extract the aging characteristic parameters to be predicted corresponding to the oil paper insulation to be predicted, and the aging characteristic parameters to be predicted are input into the target aging prediction model to obtain the predicted aging parameters output by the target aging prediction model, so that the terminal 102 determines the aging stage of the oil paper insulation to be predicted according to the predicted aging parameters. Additionally, in some embodiments, a server 104 is also included. Wherein the terminal 102 communicates with the server 104 via a network. The aging characteristic parameter to be predicted may be obtained by the terminal 102 from a database of the server 104. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a method for predicting the aging of oiled paper insulation is provided, which is described by taking the method as an example applied to the terminal in fig. 1, and includes the following steps:
and S202, acquiring aging characteristic parameters to be predicted corresponding to the insulation of the oil paper to be predicted.
The oil paper insulation can be a component in an oil-immersed transformer, and in the long-term operation process of the oil-immersed transformer, the oil paper insulation is influenced by multiple factors such as electricity, heat and environment to cause the reduction of the electrical and mechanical properties of the oil-immersed transformer, so that the aging condition of the oil paper insulation needs to be predicted in order to ensure the safety of electrical equipment. The paper oil insulation to be predicted may be paper oil insulation that requires aging prediction. The terminal 102 can obtain the aging characteristic parameter to be predicted of the oil paper insulation to be predicted. The aging characteristic parameter to be predicted can represent the aging characteristic of the oil paper insulation to be predicted, and the aging characteristic parameter to be predicted can be an effective characteristic extracted from a Raman spectrogram of the oil paper insulation to be predicted. For example, the terminal 102 may pre-select the oil paper insulation to be predicted to perform raman spectrum detection to obtain a corresponding raman spectrogram, so that the terminal 102 may extract effective features from the raman spectrogram of the oil paper insulation to be predicted to serve as aging characteristic parameters to be predicted. Among them, Raman spectroscopy (Raman spectrum) is a kind of scattering spectrum. The raman spectrum analysis method is an analysis method which is based on the raman scattering effect, analyzes the scattering spectrum with different frequency from the incident light to obtain the information of molecular vibration and rotation, and is applied to the molecular structure research.
Step S204, inputting the aging characteristic parameters to be predicted into a target aging prediction model, and acquiring predicted aging parameters output by the target aging prediction model; the target aging prediction model is obtained based on a genetic partial least square method and training of sample aging characteristic parameters and sample real aging parameters of a plurality of sample oil paper insulators.
The target aging prediction model can be a neural network model used for predicting the aging condition of the oil paper insulation, and can be obtained by training a plurality of sample aging characteristic parameters and sample real aging parameters of the oil paper insulation based on a genetic partial least square method. The terminal 102 may input the extracted aging characteristic parameters to be predicted into the target aging prediction model, so that the target aging prediction model may output corresponding predicted aging parameters according to the input aging characteristic parameters to be predicted. The predicted aging parameter can be a Raman spectrogram obtained by predicting the oil paper insulation by a target aging prediction model, the sample aging characteristic can be a characteristic input into the model when the aging prediction model is trained, and the sample aging characteristic can be obtained by extracting from the Raman spectrogram of the sample oil paper insulation; the real aging parameters of the sample can be a real Raman spectrogram obtained by performing Raman spectrum detection on the sample oiled paper insulation by the terminal 102; the Genetic partial least squares method can be a GA-PLS neural network, wherein, the GA (Genetic Algorithm) Algorithm is designed and proposed according to the evolution rule of organisms in the nature, is a calculation model of the biological evolution process simulating the natural selection and the Genetic mechanism of the Darwinian biological evolution theory, and is a method for searching the optimal solution by simulating the natural evolution process; the Partial Least Squares (PLS) is a multivariate calibration method, in the raman spectrum, a certain band is selected to participate in modeling through PLS selection characteristic variables, so as to obtain a better calibration model, and the terminal 102 can predict the aging stage of the oil paper insulation by combining the genetic algorithm and the partial least squares.
And step S206, determining a predicted aging stage corresponding to the insulation of the oil paper to be predicted according to the predicted aging parameters.
The predicted aging parameter may be a predicted raman spectrogram obtained by predicting the sample aging characteristic parameter by the terminal 102 using the target aging prediction model. The terminal 102 may determine a predicted aging stage corresponding to the oil paper insulation to be predicted according to the predicted aging parameter obtained by the prediction.
And the terminal 102 determines the predicted aging stage corresponding to the oil paper insulation to be predicted according to the predicted raman spectrogram, wherein the predicted aging parameter can be a predicted raman spectrogram corresponding to the aging characteristic parameter to be predicted. For example, in one embodiment, determining a predicted aging stage corresponding to the paper oil insulation to be predicted according to the predicted aging parameter includes: inquiring an aging stage table according to the prediction Raman spectrogram, and determining a prediction aging stage corresponding to the oil paper insulation to be predicted; the aging stage table comprises the corresponding relation between a plurality of aging stages of the oiled paper insulation and the Raman spectrogram. In this embodiment, the terminal 102 may query the aging stage table according to the predicted raman spectrogram, so that the terminal 102 may query the aging stage table to obtain the oil paper insulation aging stage corresponding to the predicted raman spectrogram. The aging stage table may include a corresponding relationship between a plurality of aging stages of the oiled paper insulation and the raman spectrogram. The terminal 102 may predict the aging stage of the oiled paper insulation by the above method, and the comparison between the specific predicted result and the actual aging result may be shown in table 1:
TABLE 1 Raman Spectroscopy diagnostic results
As can be seen from table 1, the accuracy of the prediction of the aging stage of the oilpaper insulation can be improved by the method provided in the above embodiment.
According to the oil paper insulation aging prediction method, the aging characteristic parameters to be predicted corresponding to the oil paper insulation to be predicted are obtained, the aging characteristic parameters to be predicted are input into a target aging prediction model obtained through training based on a genetic partial least square method, sample aging characteristic parameters of a plurality of sample oil paper insulations and real aging parameters of the samples, the aging prediction parameters output by the target aging prediction model are obtained, and therefore the aging prediction stage corresponding to the oil paper insulation to be predicted is determined according to the aging prediction parameters. Compared with the traditional manual observation of the aging degree of the oiled paper insulation. According to the scheme, the target aging prediction model obtained based on the genetic partial least square method training is used for predicting the aging stage of the oil paper insulation according to the aging characteristic of the oil paper insulation, so that the prediction accuracy is improved.
In one embodiment, further comprising: obtaining a plurality of sample aging characteristic parameters corresponding to a plurality of sample oil paper insulations and obtaining a plurality of sample real aging parameters corresponding to the plurality of sample oil paper insulations; acquiring an aging prediction model to be trained; the aging prediction model to be trained comprises an input layer, a hidden layer and an output layer; each layer in the aging prediction model to be trained comprises a corresponding threshold value, and each layer comprises a corresponding weight value; inputting a plurality of sample aging characteristic parameters into an input layer, and acquiring a plurality of sample prediction aging parameters output by an aging prediction model to be trained based on a hidden layer and an output layer; obtaining an error value of each sample predicted aging parameter and the corresponding sample real aging parameter, and judging whether the error value is smaller than a preset error threshold value or not; if not, respectively adjusting weights among all layers and thresholds of all layers in the aging prediction model to be trained according to the error values, and returning to the step of inputting a plurality of sample aging characteristic parameters into the input layer; if so, ending the circulation, and obtaining the target aging prediction model according to the weight values among all layers in the aging prediction model to be trained and the threshold values of all layers when the current training is ended.
In this embodiment, the terminal 102 may utilize a plurality of sample oil-paper insulations to extract a plurality of sample aging characteristic parameters from the sample oil-paper insulations, and obtain a plurality of sample real aging parameters corresponding to the sample oil-paper insulations, and train the aging prediction model to be trained according to the sample aging characteristic parameters and the sample real aging parameters. The aging prediction model may be a BP neural network, and the structure of the aging prediction model may be as shown in fig. 3, where fig. 3 is a schematic structural diagram of the aging prediction model in one embodiment. The neural network includes an input layer, a hidden layer, and an output layer, and the terminal 102 may perform training of the target aging prediction model based on the neural network. Before obtaining a plurality of sample aging characteristic parameters and a plurality of sample real aging parameters corresponding to a plurality of sample oilpaper insulations, obtaining an aging sample in advance can be performed. For example, (1) fresh insulating oil and insulating paper which are dried in advance are placed into an aging tank, the heating temperature of the aging tank is set to be 130 ℃, then transformer oil aging samples are sampled periodically, and finally 14 groups of aging samples of 1d, 2d, 3d, 4d, 6d, 8d, 10, d12, d14, d16, d20, d24, d26 and d30 are obtained, wherein 15 samples of each group account for 210 transformer oil aging samples, and the aging samples cover four stages of good insulation, early stage of aging, middle stage of aging and final stage of aging of the oil paper insulation. The terminal 102 may perform raman spectrum detection on each of the aged samples, and perform corresponding preprocessing on the detected raman spectrogram, so that the preprocessed raman spectrogram is used as a real aging parameter of each of the aged samples. In addition, in the actual prediction process, a raman spectrogram obtained by performing raman spectrum detection on the oil paper insulation to be predicted can be subjected to extraction of aging characteristic parameters to be predicted after pretreatment. The terminal 102 may also divide the aging samples into a training set and a verification set. For example, the terminal 102 may divide 210 samples into 147 training sets and 63 validation sets according to a ratio of 7:3 for the preprocessed samples by Cross-validation. So that the terminal 102 can extract the aging characteristic parameters of the sample from the oil paper insulation serving as the aging sample in the training set. For example, the terminal 102 may perform selection and modeling on data sample characteristics by using a GA-PLS method, and complete selection and dimension reduction of raman spectrum characteristic sample information.
The terminal 102 may input the extracted aging characteristic parameters of the plurality of samples into an aging prediction model to be trained, where the aging prediction model to be trained may be a BP neural network. The terminal 102 may obtain a plurality of sample predicted aging parameters output by the aging prediction model to be trained based on the hidden layer and the output layer. The sample aging parameter can be a predicted Raman spectrogram corresponding to sample oil-paper insulation, which is obtained after the aging prediction model to be trained predicts the sample aging characteristic parameter. Specifically, the terminal 102 may use the effective features extracted from the 147 training set data as the input of the BP neural network, and build and train a GA-PLS-BP neural network diagnostic model.
After obtaining the sample predicted aging parameter output by the aging prediction model to be predicted, the terminal 102 may compare the error value of the sample predicted aging parameter with the error value of the corresponding sample real aging parameter, and determine whether the error value is smaller than a preset error threshold. If not, the terminal 102 may adjust the weights between layers and the thresholds of the layers in the aging prediction model to be trained according to the error values, and return to the step of inputting the plurality of sample aging characteristic parameters into the input layer, so that the terminal 102 may train the sample aging characteristic parameters according to the aging prediction model after the weights and the thresholds are adjusted. If so, the terminal 102 may end the loop, and obtain the target aging prediction model according to the weight values between each layer and the threshold values of each layer in the aging prediction model to be trained when the current training is ended.
The training process of the aging prediction model to be trained can be based on the training of a GA-PLS algorithm. FIG. 4 is a schematic flow chart of the genetic partial least squares method according to one embodiment, as shown in FIG. 4. For the implementation of Genetic Algorithm (GA) which mainly includes several basic elements as shown in fig. 4, after the genetic iteration is terminated, all variables are rearranged according to the selection frequency, and the selected variables are obtained by plotting the selected variables with the correlation coefficient (r) to select the optimal variables. For the BP neural network, the BP neural network consists of an input layer, an implicit layer and an output layer. The learning process of the BP neural network consists of two processes of forward propagation of signals and backward propagation of errors. In forward propagation, an input sample is transmitted from an input layer, processed layer by a hidden layer, and transmitted to an output layer.
Through the embodiment, the terminal 102 can train the aging prediction model by using a genetic partial least square method, so that the aging degree of the oil-paper insulation can be predicted by using the trained target aging prediction model, and the prediction accuracy is improved.
In one embodiment, obtaining a plurality of sample true aging parameters corresponding to a plurality of sample oiled paper insulations comprises: aiming at each sample oilpaper insulation, obtaining a Raman spectrogram of the sample oilpaper insulation; and removing a spectrum base line, removing a peak and reducing noise of the Raman spectrum to obtain a target Raman spectrum corresponding to the sample oiled paper insulation, wherein the target Raman spectrum is used as a real aging parameter of the sample.
In this embodiment, the terminal 102 may perform preprocessing on a raman spectrogram detected from an oiled paper insulator, so as to obtain a corresponding real aging parameter of the sample. The terminal 102 may pre-process both the raman spectra of the oil paper insulation to be predicted and the sample oil paper insulation. For example, for sample oil paper insulation, the terminal 102 may obtain a raman spectrum of each sample oil paper insulation, and perform spectrum baseline removal, peak removal, and noise reduction processing on the raman spectrum to obtain a target raman spectrum corresponding to each sample oil paper insulation, which is used as a true aging parameter of the sample.
Wherein, the above pre-treatments can be performed based on different modes. For example, for the baseline, the terminal 102 may subtract by polynomial iterative fitting, adjust the original spectrum data by continuous comparison, and directly compare the adjusted spectrum data with points on a fitting curve, so that the calculated baseline function is closer to the actual baseline, and finally subtract the obtained baseline from the original spectrum; for noise reduction, the terminal 102 may use a cyclic three-point zeroth-order Savitzky-Golay filtering method to perform polynomial least square fitting on the internal elements of the window through a moving window, so as to obtain a value obtained after smoothing the elements at the center of the window. The method can achieve good denoising effect and well reserve important information components in the spectrum. For the peak, the terminal 102 may perform the cyclic filtering by using a three-point sliding window averaging method, and stop the cyclic filtering when the standard deviation of the filtered noise is greater than or equal to the estimated standard deviation of the noise in the original spectrum. The method can eliminate interference caused by high-intensity spikes such as cosmic rays.
Through the embodiment, the terminal 102 can remove impurities from the acquired raman spectrogram in multiple ways, so that the effectiveness of the raman spectrogram can be improved, and the accuracy of the aging degree prediction can be improved.
In one embodiment, inputting a plurality of sample aging characteristic parameters into an input layer, and obtaining a plurality of sample predicted aging parameters output by an aging prediction model to be trained based on a hidden layer and an output layer, includes: acquiring a first weight corresponding to a first node in an input layer and a second node in a hidden layer, and a first threshold of the second node; acquiring a second weight corresponding to a second node in the hidden layer and a third node in the output layer, and a second threshold of the third node; inputting the sample aging characteristic parameters into an input layer aiming at each sample aging characteristic parameter, and acquiring a first output result of the sample aging characteristic parameters in a hidden layer according to a first weight, a first threshold and a preset activation function; and obtaining a second output result of the sample aging characteristic parameter on an output layer according to the second weight, the second threshold, the first output result and a preset activation function, and obtaining a sample predicted aging parameter corresponding to the sample aging characteristic parameter.
In this embodiment, the aging prediction model may be a BP neural network, which includes an input layer, a hidden layer, and an output layer, and each layer includes a plurality of nodes. Each node of the input layer and each node of the hidden layer contain a first weight, and each node of the hidden layer contains a first threshold; and a second threshold value is contained between each node in the hidden layer and each node in the output layer, and each node in the output layer contains the second threshold value. In the training process, when obtaining the sample prediction aging parameter, the terminal 102 may obtain the first weight, the first threshold, the second weight, and the second threshold. For each sample aging characteristic parameter, the terminal 102 may input the sample aging characteristic parameter into the input layer, and obtain a first output result of each sample aging characteristic parameter in the hidden layer according to the first weight, the first threshold and the preset activation function. The first output result also needs to be input to the output layer, and the terminal 102 may obtain a second output result of each sample aging characteristic parameter at the output layer according to the second weight, the second threshold, the first output result, and the preset activation function, so that the terminal 102 may obtain a sample predicted aging parameter corresponding to each sample aging characteristic parameter according to the output result of the output layer.
The first output result may be obtained according to the output of each node in the hidden layer, and the second output result may be obtained according to the output of each node in the output layer. For example, the terminal 102 may set the weight value between node i and node j as wijThe threshold value of the node j is bjThe output value of each node is xjAnd the output value of each node is realized according to the output values of all nodes on the upper layer, the weight values of the current node and all nodes on the upper layer, the threshold value of the current node and an activation function. The specific calculation formula can be as follows:
xj＝f(Sj)；
wherein, the node i may be a node of a previous layer, the node j may be a node of a next layer corresponding to the previous layer, and f is an activation function.
Through the embodiment, the terminal 102 can obtain the predicted aging parameters by using the threshold values of each layer and the weight values among the layers in the neural network, and perform the aging stage prediction of the oil-paper insulation by using the trained neural network, so that the prediction accuracy is improved.
In one embodiment, obtaining an error value between each sample predicted aging parameter and the corresponding sample real aging parameter comprises: obtaining a root mean square error value of the sample predicted aging parameter and the corresponding real aging parameter; obtaining a corresponding prediction set correlation coefficient according to the square of the difference between the sample predicted aging parameter and the sample real aging parameter and the square of the difference between the sample real aging parameter and the average value of the sample real aging parameters; the root mean square error value and the prediction set correlation coefficient are used as error values.
In this embodiment, the terminal 102 may also determine the training accuracy during the training process. For example, by determining an error value between the sample predicted aging parameter and the corresponding sample real aging parameter. The terminal 102 may obtain a root mean square error value corresponding to the sample aging prediction parameter and the corresponding real aging parameter; the terminal 102 may further obtain a corresponding prediction set correlation coefficient according to a square of a difference between the sample predicted aging parameter and the sample real aging parameter and a square of a difference between the sample real aging parameter and an average value of the plurality of sample real aging parameters; the terminal 102 may thus use the root mean square error value and the prediction set correlation coefficient as the error values.
Specifically, to verify the accuracy of the prediction of the GA-PLS-BP neural network model, the terminal 102 may verify the cross validation root Mean Square Error (MSE) value and the prediction set correlation coefficient (R) of the model during the training process2) As an evaluation index of the model. R2Indicating the degree of correlation between the true and predicted values. The MSE can evaluate the degree of change of the data, and the smaller the value of the MSE is, the better the accuracy of the prediction model is. The specific calculation formula is as follows:
wherein the content of the first and second substances,
is a predicted value of the spectrum, y is an actual value,
and n is the number of samples which are the average value of the actual values, namely the number of the sample aging characteristic parameters input into the aging prediction model. In the actual training process, when the terminal 102 determines the error value and the preset error threshold, it may respectively determine that the root mean square error value isIf the error value is smaller than the root mean square error threshold value, whether the correlation coefficient of the prediction set is larger than the similarity threshold value or not, and if the judgment of the terminal 102 is yes, determining that the error value meets the requirement; otherwise, the requirements are not met.
Through the embodiment, the terminal 102 may determine the error value between the predicted aging parameter and the real aging parameter based on the root mean square error value and the prediction set correlation coefficient, thereby improving the prediction accuracy of the aging prediction model.
In one embodiment, adjusting the weights between layers and the thresholds of the layers in the aging prediction model to be trained according to the error values respectively includes: acquiring a corresponding error function according to the sample prediction aging parameter and the sample real aging parameter; and adjusting the first weight, the first threshold, the second weight and the second threshold based on the Widrop-Hoff learning rule and the momentum gradient descent algorithm according to the error function and the preset activation function.
In this embodiment, in the process of training the aging prediction model, the terminal 102 may adjust the weight between each layer and the threshold of each layer in the aging prediction model when the error value is not satisfactory. The terminal 102 may obtain a corresponding error function according to the sample predicted aging parameter and the sample real aging parameter. Therefore, the terminal 102 may adjust the first weight, the first threshold, the second weight, and the second threshold based on the Widrow-Hoff learning rule and the momentum gradient descent algorithm according to the error function and the preset activation function.
Wherein, the hidden layer may also be referred to as a hidden layer; the above adjustment process may be a back propagation process in the training of the BP neural network. For example, if the terminal 102 detects that the actual output of the output layer does not match the desired output, the back propagation phase of the error may be diverted. The back propagation of the error is to transmit the output error back to the input layer by layer through the hidden layer in a certain form, and distribute the error to all units of each layer, so as to obtain the error signal of each layer of units, and the error signal is used as the basis for correcting the weight of each unit. In the BP neural network, the error signal reverse transmission subprocess is based on the Widrop-Hoff learning rule. Assume all results of the output layer are djThe error function is as follows:
wherein d isjMay be the above sample predicted aging parameter, yjMay be the corresponding sample true aging parameter.
The main purpose of the BP neural network is to repeatedly modify the weight and the threshold value so as to minimize the error function value. The Widrow-Hoff learning rule is that the weight and the threshold value of the network are continuously adjusted along the steepest descending direction of the relative error square sum, according to a gradient descending method, the correction of a weight vector is in direct proportion to the gradient of E (w, b) at the current position, and for the jth output node, the method comprises the following steps:
the terminal 102 may then derive the activation function to obtain:
wherein the content of the first and second substances,
threshold b for the above node jjIs provided with
The above is the process of calculating the adjustment amount for the weight between the hidden layer and the output layer and the threshold of the output layer. For the weight adjustment from the input layer to the hidden layer and the calculation of the threshold adjustment amount of the hidden layer, the terminal 102 may assume that the weight between the kth node of the input layer and the ith node of the hidden layer, and then the terminal 102 may obtain:
wherein the content of the first and second substances,
according to the gradient descent algorithm, the terminal 102 may adjust the weight between the hidden layer and the output layer and the threshold of the hidden layer according to the following formula:
the terminal 102 may adjust the weight between the input layer and the hidden layer and the threshold of the input layer according to the following formula:
the main idea of the gradient descent method is to search for an optimal solution along a negative gradient direction, wherein the negative gradient direction is the direction in which the function value descends most quickly, if the gradient iterated to a certain position is 0, the local minimum is reached, and the parameter updating is stopped. During the actual training process, the terminal 102 may use some improved method to approach the global minimum as much as possible.
In addition, in some embodiments, the terminal 102 may further add a momentum term to the weight adjustment formula. For example, a momentum gradient descent method is used to modify the BP algorithm, when the weights between neurons are adjusted by the BP network before improvement, the weights are adjusted only according to the gradient direction of the error at the time t, and the gradient direction before the time t is not considered, so that the training process is easy to oscillate, the convergence is slow, in order to improve the training speed of the network, a momentum term is added to an adjustment formula of the weights, and if W represents a weight matrix of a certain layer and x represents an input vector of the certain layer, a weight adjustment vector expression including the momentum term is: w is aij＝wij-η1·δij·xi+αΔwij(ii) a As can be seen from the above formula, increasing the momentum term, i.e. taking out a part from the previous weight adjustment and superimposing the part to the current weight adjustment, alpha is called momentum coefficient, and is generally 0<α<1. The physical meaning represented by the momentum term reflects the previous accumulated experience, the moment t has a damping effect, and when the weight curved surface suddenly drops, the momentum term can reduce the oscillation trend and improve the training process.
Through the embodiment, the terminal 102 firstly screens out the characteristic wave number points representing the aging products of the oil paper by the GA-PLS method, then uses the characteristic wave number points as input vectors of the neural network, continuously trains the constructed neural network, adjusts the parameters, and finally obtains the optimal parameters to achieve a smaller error value. The training and construction of the whole model are carried out in a Matlab neural network toolbox, and the terminal 102 can also observe the final optimization performance through a correlation coefficient R2 and a mean square error MSE index. Therefore, the prediction accuracy of the aging degree of the oilpaper insulation can be improved.
It should be understood that although the steps in the flowcharts of fig. 2 and 4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2 and 4 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the other steps or stages.
In one embodiment, as shown in fig. 5, there is provided an oiled paper insulation aging prediction apparatus including: an acquisition module 500, an input module 502, and a prediction module 504, wherein:
the obtaining module 500 is configured to obtain an aging characteristic parameter to be predicted corresponding to the insulation of the oil paper to be predicted.
An input module 502, configured to input the aging characteristic parameter to be predicted into the target aging prediction model, and obtain a predicted aging parameter output by the target aging prediction model; the target aging prediction model is obtained based on a genetic partial least square method and training of sample aging characteristic parameters and sample real aging parameters of a plurality of sample oil paper insulators.
And the predicting module 504 is used for determining a predicted aging stage corresponding to the oil paper insulation to be predicted according to the predicted aging parameter.
In one embodiment, the above apparatus further comprises: the training module is used for acquiring a plurality of sample aging characteristic parameters corresponding to a plurality of sample oil paper insulations and acquiring a plurality of sample real aging parameters corresponding to the plurality of sample oil paper insulations; acquiring an aging prediction model to be trained; the aging prediction model to be trained comprises an input layer, a hidden layer and an output layer; each layer in the aging prediction model to be trained comprises a corresponding threshold value, and each layer comprises a corresponding weight value; inputting a plurality of sample aging characteristic parameters into an input layer, and acquiring a plurality of sample prediction aging parameters output by an aging prediction model to be trained based on a hidden layer and an output layer; obtaining an error value of each sample predicted aging parameter and the corresponding sample real aging parameter, and judging whether the error value is smaller than a preset error threshold value or not; if not, respectively adjusting weights among all layers and thresholds of all layers in the aging prediction model to be trained according to the error values, and returning to the step of inputting a plurality of sample aging characteristic parameters into the input layer; if so, ending the circulation, and obtaining the target aging prediction model according to the weight values among all layers in the aging prediction model to be trained and the threshold values of all layers when the current training is ended.
In an embodiment, the training module is specifically configured to acquire, for each sample oiled paper insulation, a raman spectrum of the sample oiled paper insulation; and removing a spectrum base line, removing a peak and reducing noise of the Raman spectrum to obtain a target Raman spectrum corresponding to the sample oiled paper insulation, wherein the target Raman spectrum is used as a real aging parameter of the sample.
In an embodiment, the training module is specifically configured to obtain a first weight corresponding to a first node in the input layer and a second node in the hidden layer, and a first threshold of the second node;
acquiring a second weight corresponding to a second node in the hidden layer and a third node in the output layer, and a second threshold of the third node; inputting the sample aging characteristic parameters into an input layer aiming at each sample aging characteristic parameter, and acquiring a first output result of the sample aging characteristic parameters in a hidden layer according to a first weight, a first threshold and a preset activation function; and obtaining a second output result of the sample aging characteristic parameter on an output layer according to the second weight, the second threshold, the first output result and a preset activation function, and obtaining a sample predicted aging parameter corresponding to the sample aging characteristic parameter.
In an embodiment, the training module is specifically configured to obtain a root mean square error value of the sample predicted aging parameter and the corresponding real aging parameter; obtaining a corresponding prediction set correlation coefficient according to the square of the difference between the sample predicted aging parameter and the sample real aging parameter and the square of the difference between the sample real aging parameter and the average value of the sample real aging parameters; the root mean square error value and the prediction set correlation coefficient are used as error values.
In an embodiment, the training module is specifically configured to obtain a corresponding error function according to a sample predicted aging parameter and a sample real aging parameter; and adjusting the first weight, the first threshold, the second weight and the second threshold based on the Widrop-Hoff learning rule and the momentum gradient descent algorithm according to the error function and the preset activation function.
In an embodiment, the prediction module is specifically configured to query an aging stage table according to a prediction raman spectrogram, and determine a prediction aging stage corresponding to the insulation of the oil paper to be predicted; the aging stage table comprises the corresponding relation between a plurality of aging stages of the oiled paper insulation and the Raman spectrogram.
For specific limitations of the oil paper insulation aging prediction device, reference may be made to the above limitations of the oil paper insulation aging prediction method, and details are not repeated here. All or part of the modules in the oil paper insulation aging prediction device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of predicting degradation of an oil paper insulation. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the oil paper insulation aging prediction method when executing the computer program.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the above-mentioned method of predicting the degradation of an insulation of an oil paper.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for predicting the insulation aging of oiled paper is characterized by comprising the following steps:
acquiring aging characteristic parameters to be predicted corresponding to the insulation of the oil paper to be predicted;
inputting the aging characteristic parameters to be predicted into a target aging prediction model, and acquiring predicted aging parameters output by the target aging prediction model; the target aging prediction model is obtained based on a genetic partial least square method and training of sample aging characteristic parameters and sample real aging parameters of a plurality of sample oil paper insulators;
and determining a prediction aging stage corresponding to the insulation of the oil paper to be predicted according to the prediction aging parameters.
2. The method of claim 1, further comprising:
obtaining a plurality of sample aging characteristic parameters corresponding to a plurality of sample oil paper insulations and obtaining a plurality of sample real aging parameters corresponding to the plurality of sample oil paper insulations;
acquiring an aging prediction model to be trained; the aging prediction model to be trained comprises an input layer, a hidden layer and an output layer; each layer in the aging prediction model to be trained comprises a corresponding threshold value, and each layer comprises a corresponding weight value;
inputting the plurality of sample aging characteristic parameters into the input layer, and acquiring a plurality of sample prediction aging parameters output by the aging prediction model to be trained based on the hidden layer and the output layer;
obtaining an error value of each sample predicted aging parameter and a corresponding sample real aging parameter, and judging whether the error value is smaller than a preset error threshold value or not;
if not, respectively adjusting weights among all layers and thresholds of all layers in the aging prediction model to be trained according to the error value, and returning to the step of inputting the aging characteristic parameters of the multiple samples into the input layer;
if so, ending circulation, and obtaining the target aging prediction model according to the weight values among all layers and the threshold values of all layers in the aging prediction model to be trained when the current training is ended.
3. The method of claim 2, wherein said obtaining a plurality of sample true aging parameters corresponding to said plurality of sample oiled paper insulations comprises:
aiming at each sample oilpaper insulation, obtaining a Raman spectrogram of the sample oilpaper insulation;
and removing a spectrum base line, removing a peak and reducing noise of the Raman spectrum to obtain a target Raman spectrum corresponding to the sample oiled paper insulation, wherein the target Raman spectrum is used as a real aging parameter of the sample.
4. The method according to claim 2, wherein the inputting the plurality of sample aging characteristic parameters into the input layer, and obtaining the plurality of sample predicted aging parameters output by the aging prediction model to be trained based on the hidden layer and the output layer comprises:
acquiring a first weight corresponding to a first node in the input layer and a second node in the hidden layer, and a first threshold of the second node;
acquiring a second weight corresponding to a second node in the hidden layer and a third node in the output layer, and a second threshold of the third node;
inputting the sample aging characteristic parameters into the input layer aiming at each sample aging characteristic parameter, and acquiring a first output result of the sample aging characteristic parameters in the hidden layer according to the first weight, the first threshold and a preset activation function;
and obtaining a second output result of the sample aging characteristic parameter on the output layer according to the second weight, the second threshold, the first output result and the preset activation function, and obtaining a sample predicted aging parameter corresponding to the sample aging characteristic parameter.
5. The method of claim 2, wherein obtaining an error value for each sample predicted aging parameter and the corresponding sample true aging parameter comprises:
obtaining a root mean square error value of the sample predicted aging parameter and a corresponding real aging parameter;
obtaining a corresponding prediction set correlation coefficient according to the square of the difference between the sample predicted aging parameter and the sample real aging parameter and the square of the difference between the sample real aging parameter and the average value of the sample real aging parameters;
and taking the root mean square error value and the prediction set correlation coefficient as the error value.
6. The method according to claim 4, wherein the adjusting the weights between layers and the thresholds of the layers in the aging prediction model to be trained according to the error value respectively comprises:
acquiring a corresponding error function according to the sample predicted aging parameter and the sample real aging parameter;
and adjusting the first weight, the first threshold, the second weight and the second threshold based on a Widrop-Hoff learning rule and a momentum gradient descent algorithm according to the error function and a preset activation function.
7. The method according to claim 1, wherein the predicted aging parameter is a predicted raman spectrogram corresponding to the aging characteristic parameter to be predicted;
and determining a prediction aging stage corresponding to the oil paper insulation to be predicted according to the prediction aging parameter, wherein the prediction aging stage comprises the following steps:
inquiring an aging stage table according to the prediction Raman spectrogram, and determining a prediction aging stage corresponding to the oil paper insulation to be predicted; the aging stage table comprises the corresponding relation between a plurality of aging stages of the oilpaper insulation and the Raman spectrogram.
8. An apparatus for predicting degradation of oiled paper insulation, the apparatus comprising:
the acquisition module is used for acquiring the aging characteristic parameters to be predicted corresponding to the insulation of the oil paper to be predicted;
the input module is used for inputting the aging characteristic parameters to be predicted into a target aging prediction model and acquiring predicted aging parameters output by the target aging prediction model; the target aging prediction model is obtained based on a genetic partial least square method and training of sample aging characteristic parameters and sample real aging parameters of a plurality of sample oil paper insulators;
and the prediction module is used for determining a prediction aging stage corresponding to the oil paper insulation to be predicted according to the prediction aging parameter.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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