CN114354880A - Cable aging life prediction method and system based on attention BP neural network - Google Patents
Cable aging life prediction method and system based on attention BP neural network Download PDFInfo
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 77
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000012549 training Methods 0.000 claims abstract description 25
- 238000012360 testing method Methods 0.000 claims description 15
- 238000012423 maintenance Methods 0.000 claims description 14
- 230000007613 environmental effect Effects 0.000 claims description 11
- 210000002569 neuron Anatomy 0.000 claims description 11
- 230000004913 activation Effects 0.000 claims description 8
- 230000007547 defect Effects 0.000 claims description 7
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- 238000005286 illumination Methods 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 5
- 239000001301 oxygen Substances 0.000 claims description 5
- 229910052760 oxygen Inorganic materials 0.000 claims description 5
- 210000002364 input neuron Anatomy 0.000 claims description 4
- 210000004205 output neuron Anatomy 0.000 claims description 4
- 238000013213 extrapolation Methods 0.000 claims description 3
- 238000013519 translation Methods 0.000 claims description 3
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- 230000003647 oxidation Effects 0.000 description 2
- 238000007254 oxidation reaction Methods 0.000 description 2
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Abstract
The invention discloses a cable aging life prediction method based on an attention BP neural network, which comprises the following steps: (1) acquiring information of a cable sample and obtaining a training sample by measuring the residual life of the cable sample; (2) designing and training an attention BP neural network; (3) and inputting the information of the cable to be tested into the trained attention BP neural network to obtain the service life of the cable to be tested. The invention also discloses a cable aging life prediction system based on the attention BP neural network. The invention introduces an attention mechanism, evaluates the service life of the cable by a plurality of parameters of two kinds of information, avoids single reference quantity selected and improves the evaluation precision; the structure of the cable can not be damaged, reference is provided for the replacement of the cable, the complex procedures for replacing the cable are reduced, the waste of materials can be reduced, the cost is saved, and the accident potential is reduced.
Description
Technical Field
The invention relates to a cable aging life prediction method and a system, in particular to a cable aging life prediction method and a system based on an attention BP neural network.
Background
The cable plays an extremely important role in the transmission of electric energy of the existing power system, and the running state of the cable directly influences the safety and stability of the power system. The design life of cable generally is 20 to 30 years, along with the increase of cable service life, its insulation can take place ageing gradually, and whole insulating state progressively degrades, and to the cable of operation many years, its whole insulation is ageing comparatively serious, simultaneously, if the operational environment of cable is comparatively abominable, if moist, contain corrosive substance, radiation serious, its whole insulating ageing will be more rapid, the service life of cable this moment is probably less than design life. Therefore, the evaluation of the insulation aging state of the cable is particularly important, and if the insulation aging state of the cable cannot be evaluated well so as to accurately estimate the replacement time of the cable, the fault of the cable is easily caused, and once the fault of the cable occurs, the shutdown and even the out-of-control of a large-scale electrical system are caused, so that the serious economic loss and the social influence are caused.
In order to improve the safe reliability of the operation of the cable in the last two decades, various methods for detecting the cable are formed. One of the most prominent detection methods is the periodic insulation preventative testing. For example, patent CN201511018070.0 discloses a method for evaluating the insulation aging state of a cable, which comprises slicing a cable sample, selecting two temperature points of 140 ℃ and 160 ℃ for thermal aging, taking out the sample after aging, standing at room temperature for 24 hours, performing a stretching experiment, a differential scanning calorimetry experiment, an infrared spectroscopy experiment and a thermogravimetric experiment on the aged sample, and obtaining related data parameters; according to the data parameters, representing the insulation aging state of the cable so as to evaluate the insulation aging state of the cable; the method needs to sample under the condition of power failure, and the cable sample wafer needs to be tested repeatedly in each test, so that the operation and maintenance cost and the evaluation cost are increased.
Patent CN 110286303 a discloses a cable insulation aging state evaluation method, in which characteristic parameters of a measured cable and an evaluation result form a data set, which is input to a BP neural network for training, and the trained neural network is used for prediction. The method can evaluate the insulation aging state of the cable to a certain degree, but the selected reference quantity is single, and the real aging condition of the cable insulation cannot be completely reflected.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a cable aging life prediction method and system based on an attention BP neural network, and solves the problems that the selected reference amount is single, the real aging condition of cable insulation cannot be completely reflected, and the prediction precision is low.
The technical scheme is as follows: the cable aging life prediction method based on the attention BP neural network comprises the following steps:
(1) acquiring information of a cable sample and obtaining a training sample by measuring the service life of the cable sample;
(2) designing and training an attention BP neural network;
(3) and inputting the information of the cable to be tested into the trained attention BP neural network to obtain the service life of the cable to be tested.
The information of the cable sample comprises environment information and operation and maintenance information; the environmental information comprises temperature, humidity, illumination intensity and oxygen concentration; the operation and maintenance information comprises current-carrying capacity, average load percentage, service life, family defect influence factors and historical failure times.
And (2) testing the service life of the cable sample by adopting an accelerated aging test in the step (1), testing the service life of the cable sample by increasing the temperature, combining with an arrhenius equation, and obtaining the service life of the cable sample under the actual temperature condition by using an extrapolation equation for the service life measured under the high-temperature condition through translation fitting.
The attention BP neural network comprises an attention unit and a neural network unit, and the output of the attention unit is connected with the input of the neural network unit.
The attention module unit comprises two cascaded full-connection layers, wherein the first full-connection layer adopts a RELU (remote unified resource locator) activation function, and the second full-connection layer adopts a Sigmoid activation function; and after the input data passes through the full connection layer, Scale operation is carried out on the input data which does not pass through the full connection layer, so that the output data of the attention module is obtained.
The neural network unit comprises a 4-layer BP neural network, and the BP neural network comprises 1 input layer, 2 hidden layers and an output layer.
The hidden layer neuron number is
And determining, wherein L is the number of hidden layer neurons, n is the number of input neurons, m is the number of output neurons, and a is a constant between 1 and 10.
The cable aging life prediction system based on the attention BP neural network comprises a training sample module, an attention BP neural network module and a prediction module; the training sample module acquires information of a cable sample, acquires the residual life of the cable sample through measurement to construct a training sample, and trains the attention BP neural network module; the attention BP neural network module comprises an attention unit and a neural network unit, and the output of the attention unit is connected with the input of the neural network unit; and the prediction module inputs the information of the cable to be tested into the trained attention BP neural network module to obtain the service life of the cable to be tested.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
(1) the invention introduces an attention mechanism, evaluates the service life of the cable through a plurality of parameters of two kinds of information, and improves the evaluation precision.
(2) The structure of the cable is not damaged, reference is provided for the replacement of the cable, the complex procedures for replacing the cable are reduced, the waste of materials can be reduced, the cost is saved, and the accident potential is reduced.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of an attention BP neural network of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As can be seen from fig. 1, the cable aging life prediction method based on the attention BP neural network according to the present invention includes the following steps:
the method comprises the following steps of (1) collecting a plurality of groups of cable samples, recording the environmental information and the operation and maintenance information of each sample, and obtaining the training samples by measuring the residual life of the cable samples.
And taking the environmental information and the operation and maintenance information of the cable sample as independent variables, and forming a training sample by using the independent variables. The environmental information includes temperature, humidity, illumination intensity, and oxygen concentration. For temperature, humidity, illumination intensity and oxygen concentration information in the environmental information. Considering that the environmental information of spring, summer, autumn and winter all have great changes, the environmental information of the cable sample can adopt the average value of the service years in a certain year all the year. The operation and maintenance information comprises carrying capacity, average load percentage, service life, family defect influence factors and historical failure times. And taking the average value of the current capacity and the average load percentage in the operation and maintenance information from service. Whether the family defect influence in the operation and maintenance information is equivalent to a number or not, the non-family defect influence is 0, and the family defect influence is 1.
And selecting 50-200 cable sample data with different environment information and operation and maintenance information as training samples to train the established BP neural network.
In this embodiment, 100 sets of parameter data are used to train the three-layer BP neural network. The detection of the environmental parameters of the cable has errors, the selected data is too little, and certain randomness exists, so that the error of the trained neural network is larger, and the error of life prediction is also larger; excessive data can increase the cost of sampling and data collection, 100 groups of data can avoid the training error of the neural network, and the cost of data collection is reasonably controlled.
And establishing an experimental environment, simulating parameter values of a cable using environment through an accelerated aging test, and measuring the service life of the cable. Because the natural service life of the cable is generally 10 to 30 years, the natural aging test is not practical for testing the service life of the cable, the service life of a cable sample is tested by adopting an accelerated aging test, the temperature is increased, then the service life of the cable sample is tested, the arrhenius equation is combined, the translation fitting is carried out, the service life measured under the high temperature condition is calculated by an extrapolation equation, and the service life is the residual service life value of the cable sample obtained by experimental measurement.
The accelerated aging test comprises the following specific steps:
carrying out accelerated thermal oxidation aging treatment on each group of 4 cable samples at different temperatures and different times to simulate the cable aging process; and simultaneously acquiring the breaking elongation of the cable insulation sample at different temperatures and aging times, carrying out a mechanical strength test according to a GB/T2951.11-2008 test method, measuring how long the breaking elongation of the sample is changed into +/-50% before thermal aging, wherein the aging time is the aging life of the cable at the current temperature, and thus obtaining the corresponding aging end time tau at different temperatures T. The accelerated thermal oxidative aging test of this example uses four temperature points of 135 deg.C, 150 deg.C, 165 deg.C and 180 deg.C.
And deducing a linear equation between the service life tau of the cable sample to be tested and the reciprocal 1/T of the absolute temperature of accelerated aging according to an Arrhenius equation:
wherein T is the aging temperature of the sample cable sample and is expressed in K; r is a gas constant with a value of 8.314 in units of J/(mol. K); eaThe linear equation has a slope of J/mol as the activation energy of the cable insulation sample
The intercept is a. Fitting the equation according to 4 groups of different temperatures T obtained in the accelerated thermal oxidation aging treatment and the aging endpoint time tau at the temperature to obtain the slope and intercept of the linear equation. Then, bringing the actual running temperature of the cable sample into T to obtain tau, namely the residual service life of the group of cable samples, wherein the unit is h;
and (2) training an attention BP neural network through the cable sample and the service life of the cable sample.
And establishing an attention module and a 4-layer BP neural network. The attention module is a network structure capable of performing an attention mechanism, and the attention module structure is shown in fig. 2. Input data passes through two full-connection layers, wherein the first full-connection layer adopts a RELU activation function, and the second full-connection layer adopts a Sigmoid activation function. And after the input data passes through the full connection layer, carrying out Scale operation on the input data which does not pass through the structure to obtain data subjected to attention mechanism. The 4-layer BP neural network comprises 1 input layer, 2 hidden layers and one output layer. Wherein, the number of the neurons in the input layer is 9, the parameters are matched with the number of the neurons in the 9 input layers, and the number of the neurons in the output layer is 1, namely the predicted life is output. Hidden layer neuron number design according to empirical formula
In the formula, L is the number of hidden layer neurons, n is the number of input neurons, m is the number of output neurons, and a is a constant between 1 and 10. In this embodiment, the hidden layer has 2 layers, the number of neurons in each layer is 10, and the activation function is RELU. And simultaneously adding a BN operation and a Dropout operation between two hidden layers to avoid data overfitting. Attention BP neural network overall structure as shown in FIG. 2, training samples are firstly input into an attention module, and then output data of the attention module is used as input to be input into the BP neural network for model training.
The attention BP neural network calculates according to a training sample to obtain a cable life value and compares the cable life value with a life value obtained by experimental measurement, adjusts the weight and the threshold value of the attention BP neural network according to the difference value between the calculated value and the actual value, and repeats the training step until the difference value between the calculated value and the actual value is small enough or zero, thereby determining the weight and the threshold value of the middle layer of the attention BP neural network; and obtaining the trained attention BP artificial neural network.
And (3) selecting a cable with the service life to be predicted, inputting the environmental information and the operation and maintenance information of the cable to be predicted into the trained attention BP neural network, and calculating by the neural network to obtain a prediction result of the residual service life of the cable.
The cable aging life prediction system based on the attention BP neural network comprises a training sample module, an attention BP neural network module and a prediction module; the training sample module acquires information of a cable sample, acquires the residual life of the cable sample through measurement to construct a training sample, and trains the attention BP neural network module; the attention BP neural network module comprises an attention unit and a neural network unit, and the output of the attention unit is connected with the input of the neural network unit; and the prediction module inputs the information of the cable to be tested into the trained attention BP neural network module to obtain the service life of the cable to be tested.
Claims (10)
1. A cable aging life prediction method based on an attention BP neural network is characterized in that: the method comprises the following steps:
(1) acquiring information of a cable sample and obtaining a training sample by measuring the service life of the cable sample;
(2) designing and training an attention BP neural network;
(3) and inputting the information of the cable to be tested into the trained attention BP neural network to obtain the service life of the cable to be tested.
2. The cable aging life prediction method based on attention BP neural network as claimed in claim 1, characterized in that: the information of the cable sample comprises environment information and operation and maintenance information;
the environmental information comprises temperature, humidity, illumination intensity and oxygen concentration;
the operation and maintenance information comprises current-carrying capacity, average load percentage, service life, family defect influence factors and historical failure times.
3. The cable aging life prediction method based on attention BP neural network as claimed in claim 1, characterized in that: and (2) testing the service life of the cable sample by adopting an accelerated aging test in the step (1), testing the service life of the cable sample by increasing the temperature, combining with an arrhenius equation, and obtaining the service life of the cable sample under the actual temperature condition by using an extrapolation equation for the service life measured under the high-temperature condition through translation fitting.
4. The cable aging life prediction method based on attention BP neural network as claimed in claim 1, characterized in that: the attention BP neural network comprises an attention unit and a neural network unit, and the output of the attention unit is connected with the input of the neural network unit.
5. The method for predicting the aging life of a cable based on an attention BP neural network according to claim 4, wherein: the attention module unit comprises two cascaded full-connection layers, wherein the first full-connection layer adopts a RELU (remote unified resource locator) activation function, and the second full-connection layer adopts a Sigmoid activation function; and after the input data passes through the full connection layer, Scale operation is carried out on the input data which does not pass through the full connection layer, so that the output data of the attention module is obtained.
6. The method for predicting the aging life of a cable based on an attention BP neural network according to claim 4, wherein: the neural network unit comprises a 4-layer BP neural network, and the BP neural network comprises 1 input layer, 2 hidden layers and an output layer.
7. The method for predicting the aging life of a cable based on an attention BP neural network as claimed in claim 6, wherein: the hidden layer neuron number is
And determining, wherein L is the number of hidden layer neurons, n is the number of input neurons, m is the number of output neurons, and a is a constant between 1 and 10.
8. A cable aging life prediction system based on an attention BP neural network is characterized in that: the system comprises a training sample module, an attention BP neural network module and a prediction module;
the training sample module acquires information of a cable sample, acquires the residual life of the cable sample through measurement to construct a training sample, and trains the attention BP neural network module;
the attention BP neural network module comprises an attention unit and a neural network unit, and the output of the attention unit is connected with the input of the neural network unit;
and the prediction module inputs the information of the cable to be tested into the trained attention BP neural network module to obtain the service life of the cable to be tested.
9. The attention BP neural network-based cable aging life prediction system of claim 8, wherein: the information of the cable sample comprises environment information and operation and maintenance information; the environmental information comprises temperature, humidity, illumination intensity and oxygen concentration; the operation and maintenance information comprises current-carrying capacity, average load percentage, service life, family defect influence factors and historical failure times.
10. The attention BP neural network-based cable aging life prediction system of claim 8, wherein: the neural network unit comprises a 4-layer BP neural network, and the BP neural network comprises 1 input layer, 2 hidden layers and an output layer; the hidden layer neuron number is
Determining, wherein L is a hidden layer neuronThe number, n is the number of input neurons, m is the number of output neurons, and a is a constant between 1 and 10.
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- 2021-11-26 CN CN202111419121.6A patent/CN114354880A/en active Pending
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