CN115659851A - Temperature rise evaluation method and device for cable terminal - Google Patents
Temperature rise evaluation method and device for cable terminal Download PDFInfo
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Abstract
The invention discloses a temperature rise evaluation method and a temperature rise evaluation device for a cable terminal, wherein the method comprises the following steps: the method comprises the steps of obtaining temperature rise risk data of a cable terminal head to be detected and a temperature rise risk training sample of a cable database, extracting temperature rise risk influence factor data from the temperature rise risk data, establishing a temperature rise risk evaluation model according to the temperature rise risk training sample through a genetic algorithm, inputting the temperature rise risk influence factor data into the temperature rise risk evaluation model, calculating to obtain temperature rise risk grade data of the cable terminal head to be detected, and comparing the temperature rise risk grade data with a preset temperature rise threshold value to obtain temperature rise risk evaluation result data of the cable terminal head to be detected. The method is beneficial to solving the technical problems that the existing cable terminal temperature rise evaluation method has the defects of slow convergence, easy falling into local optimization and the like during parameter optimization due to the adoption of a BP neural network, and the accuracy of the cable terminal temperature rise evaluation is improved.
Description
Technical Field
The invention relates to the technical field of temperature of cable terminals, in particular to a temperature rise evaluation method and device for a cable terminal.
Background
With the increasing of social power load, the use of power cables in power transmission and distribution systems is increased year by year, the cable terminal is the insulation weak part of the power cable, and due to the poor field construction, the large difficulty of process manufacture, the aging of elements and the like, the phenomena of overheating and discharging are easy to occur, the insulation degradation is accelerated, the service life loss of cable terminal equipment is prolonged, and even serious accidents such as fire disasters are caused. Therefore, potential safety hazards caused by overheating are checked and processed in time, and the research on the temperature rise risk assessment method of the single-core cable terminal is an important part for avoiding overheating faults and influencing the power transmission and distribution reliability of the power system.
The machine learning algorithm is gradually applied to various fields of electrical engineering and the like, and the BP neural network is a multi-layer feedforward network trained according to an error inverse propagation algorithm and is one of the most widely applied neural network models at present. The BP neural network has stronger nonlinear mapping capability, can realize the mapping function from one/a plurality of inputs to a plurality of outputs, and is suitable for the problem of complex internal mechanism. The BP neural network can automatically memorize the learning content in the weight of the network, but under the conditions of excessive data and external environment interference, such as the change of convection heat transfer coefficient caused by the change of the ambient temperature and the wind speed outside the single-core cable terminal, the self-adaption and self-learning capabilities of the BP neural network are weak. In addition, the optimized objective function is generally complex, and therefore, the convergence speed is slow.
The self-adaptive BP neural network introduces self-learning and self-adaptive capabilities of an adaptive genetic algorithm enhanced model on the basis of the BP neural network, can quickly learn and timely and adaptively adjust cross and variation probability and network parameters under the condition of external interference, accelerates convergence speed, and simultaneously avoids the problems that the BP neural network falls into local optimum, overfitting and the like.
Therefore, in order to improve the accuracy of the temperature rise evaluation of the cable terminal and solve the technical problems that the convergence is slow and the local optimization is easy to occur in the parameter optimization of the existing temperature rise evaluation method of the cable terminal due to the adoption of the BP neural network, the temperature rise evaluation method of the cable terminal needs to be constructed.
Disclosure of Invention
The invention provides a method and a device for evaluating the temperature rise of a cable terminal, which solve the technical problems that the convergence is slow and the local optimization is easy to occur in the parameter optimization of the existing method for evaluating the temperature rise of the cable terminal due to the adoption of a BP (back propagation) neural network.
In a first aspect, the present invention provides a method for evaluating a temperature rise of a cable termination, including:
acquiring temperature rise risk data of a cable terminal to be tested and a temperature rise risk training sample of a cable database; the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal head and a corresponding sample class label;
extracting temperature rise risk influence factor data from the temperature rise risk data;
establishing a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category label through a genetic algorithm;
inputting the temperature rise risk influence factor data into the temperature rise risk evaluation model, and calculating to obtain temperature rise risk grade data of the cable terminal head to be detected;
and comparing the temperature rise risk grade data with a preset temperature rise threshold value to obtain temperature rise risk evaluation result data of the cable terminal to be tested.
Optionally, obtain the temperature rise risk data of the cable terminal that awaits measuring to and the cable temperature rise risk training sample of cable database, include:
acquiring initial temperature rise risk data of a cable terminal to be tested and an initial temperature rise risk training sample of a cable database;
and normalizing the initial temperature rise risk data and the initial temperature rise risk training sample to obtain the temperature rise risk data of the cable terminal head to be tested and the temperature rise risk training sample of the cable database.
Optionally, establishing a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category label through a genetic algorithm, wherein the temperature rise risk evaluation model comprises the following steps:
establishing a preliminary temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category label;
training the preliminary temperature rise risk evaluation model through the genetic algorithm based on the historical temperature rise risk data and the corresponding sample category label to obtain a trained preliminary temperature rise risk evaluation model;
and verifying the trained preliminary temperature rise risk evaluation model based on the historical temperature rise risk data and the corresponding sample category label to obtain the temperature rise risk evaluation model.
Optionally, training the preliminary temperature rise risk evaluation model based on the historical temperature rise risk data and the corresponding sample category label through the genetic algorithm to obtain a trained preliminary temperature rise risk evaluation model, including:
inputting temperature rise risk influence factor data in the historical temperature rise risk data into the preliminary temperature rise risk evaluation model to generate a corresponding sample category;
determining a training error according to the historical temperature rise risk data, the corresponding sample class label and the sample class;
and adjusting the preliminary temperature rise risk evaluation model through the genetic algorithm based on the training error to obtain an optimal parameter, and optimizing the preliminary temperature rise risk evaluation model by adopting the optimal parameter to obtain the trained preliminary temperature rise risk evaluation model.
Optionally, before inputting the temperature rise risk influencing factor data in the historical temperature rise risk data into the preliminary temperature rise risk evaluation model and generating the corresponding sample category, the method further includes:
and initializing parameters of the preliminary temperature rise risk evaluation model.
In a second aspect, the present invention provides a temperature rise evaluation device for a cable termination, including:
the acquisition module is used for acquiring temperature rise risk data of the cable terminal head to be detected and a temperature rise risk training sample of a cable database; the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal head and a corresponding sample class label;
the extraction module is used for extracting temperature rise risk influence factor data from the temperature rise risk data;
the establishing module is used for establishing a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category label through a genetic algorithm;
the calculation module is used for inputting the temperature rise risk influence factor data into the temperature rise risk evaluation model and calculating to obtain temperature rise risk grade data of the cable terminal head to be detected;
and the comparison module is used for comparing the temperature rise risk grade data with a preset temperature rise threshold value to obtain temperature rise risk evaluation result data of the cable terminal head to be tested.
Optionally, the obtaining module includes:
the acquisition submodule is used for acquiring initial temperature rise risk data of the cable terminal to be detected and an initial temperature rise risk training sample of a cable database;
and the processing submodule is used for carrying out normalization processing on the initial temperature rise risk data and the initial temperature rise risk training sample to obtain the temperature rise risk data of the cable terminal head to be tested and the temperature rise risk training sample of the cable database.
Optionally, the establishing module includes:
the establishing submodule is used for establishing a preliminary temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category labels;
the training submodule is used for training the preliminary temperature rise risk evaluation model through the genetic algorithm based on the historical temperature rise risk data and the corresponding sample category label to obtain a trained preliminary temperature rise risk evaluation model;
and the verification submodule is used for verifying the trained preliminary temperature rise risk evaluation model based on the historical temperature rise risk data and the corresponding sample category label to obtain the temperature rise risk evaluation model.
Optionally, the training submodule includes:
the generating unit is used for inputting temperature rise risk influence factor data in the historical temperature rise risk data into the preliminary temperature rise risk evaluation model and generating a corresponding sample type;
the error unit is used for determining a training error according to the historical temperature rise risk data, the corresponding sample class label and the sample class;
and the optimization unit is used for adjusting the preliminary temperature rise risk evaluation model through the genetic algorithm based on the training error to obtain an optimal parameter, and optimizing the preliminary temperature rise risk evaluation model by adopting the optimal parameter to obtain the trained preliminary temperature rise risk evaluation model.
Optionally, the training sub-module further comprises:
and the initial unit is used for initializing the parameters of the preliminary temperature rise risk evaluation model.
According to the technical scheme, the invention has the following advantages: the invention provides a temperature rise evaluation method of a cable terminal, which comprises the steps of obtaining temperature rise risk data of a cable terminal to be tested and a temperature rise risk training sample of a cable database, wherein the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal and a corresponding sample type label, extracting temperature rise risk influence factor data from the temperature rise risk data, establishing a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample type label through a genetic algorithm, inputting the temperature rise risk influence factor data into the temperature rise risk evaluation model, calculating temperature rise risk grade data of the cable terminal to be tested, comparing the temperature rise risk grade data with a preset temperature rise threshold value to obtain temperature rise risk evaluation result data of the cable terminal to be tested, and solving the technical problems that the existing cable terminal temperature rise evaluation method has the defects of slow convergence, easy local optimization and the like due to the adoption of a BP neural network, and improving the accuracy of temperature rise evaluation of the cable terminal.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a flowchart illustrating a first embodiment of a method for evaluating a temperature rise of a cable termination according to the present invention;
fig. 2 is a flowchart illustrating a second embodiment of a temperature rise evaluation method for a cable termination according to the present invention;
FIG. 3 is a block diagram of a temperature rise risk assessment model according to the present invention;
fig. 4 is a block diagram of a temperature rise evaluation apparatus of a cable termination according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method and a device for evaluating the temperature rise of a cable terminal, which are used for solving the technical problems that the existing method for evaluating the temperature rise of the cable terminal has the defects of slow convergence, easy falling into local optimization and the like during parameter optimization due to the adoption of a BP neural network.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a first embodiment, referring to fig. 1, fig. 1 is a flowchart of a first temperature rise evaluation method for a cable termination according to a first embodiment of the present invention, including:
step S101, acquiring temperature rise risk data of a cable terminal to be tested and a temperature rise risk training sample of a cable database; the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal head and a corresponding sample class label;
in the embodiment of the invention, the initial temperature rise risk data of the cable terminal head to be tested and the initial temperature rise risk training sample of the cable database are obtained, and the initial temperature rise risk data and the initial temperature rise risk training sample are subjected to normalization processing to obtain the temperature rise risk data of the cable terminal head to be tested and the temperature rise risk training sample of the cable database.
Step S102, extracting temperature rise risk influence factor data from the temperature rise risk data;
step S103, establishing a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category label through a genetic algorithm;
in the embodiment of the invention, a preliminary temperature rise risk evaluation model is established according to the historical temperature rise risk data and the corresponding sample type label, the preliminary temperature rise risk evaluation model is trained through the genetic algorithm based on the historical temperature rise risk data and the corresponding sample type label to obtain the trained preliminary temperature rise risk evaluation model, and the trained preliminary temperature rise risk evaluation model is verified based on the historical temperature rise risk data and the corresponding sample type label to obtain the temperature rise risk evaluation model.
Step S104, inputting the temperature rise risk influence factor data into the temperature rise risk evaluation model, and calculating to obtain temperature rise risk grade data of the cable terminal to be tested;
step S105, comparing the temperature rise risk grade data with a preset temperature rise threshold value to obtain temperature rise risk evaluation result data of the cable terminal to be detected;
the temperature rise evaluation method for the cable terminal provided by the embodiment of the invention comprises the steps of obtaining temperature rise risk data of the cable terminal to be evaluated and a temperature rise risk training sample of a cable database, wherein the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal and a corresponding sample type label, extracting temperature rise risk influencing factor data from the temperature rise risk data, establishing a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample type label through a genetic algorithm, inputting the temperature rise risk influencing factor data into the temperature rise risk evaluation model, calculating temperature rise risk grade data of the cable terminal to be evaluated, comparing the temperature rise risk grade data with a preset temperature rise threshold value, obtaining temperature rise risk evaluation result data of the cable terminal to be evaluated, solving the technical problem that the existing cable terminal evaluation method has the defects of slow convergence, local optimization and the like during parameter optimization due to the adoption of a BP neural network, and improving the accuracy of temperature rise evaluation of the cable terminal.
In a second embodiment, referring to fig. 2, fig. 2 is a flowchart illustrating a method for evaluating a temperature rise of a cable termination according to the present invention, including:
step S201, acquiring initial temperature rise risk data of a cable terminal to be tested and an initial temperature rise risk training sample of a cable database;
in the embodiment of the invention, the initial temperature rise risk data of the cable terminal head to be tested and the initial temperature rise risk training sample of the cable database are obtained.
In the specific implementation, for a single-core cable, the external environment temperature of the cable, the surface wind speed of the terminal head, the magnitude of load-bearing current and the magnitude of conductor contact resistance directly influence the internal temperature distribution condition of the terminal head, and can be used as a direct risk assessment influence factor. In addition, the contact resistance is influenced by different materials, different resistivity and different contact area of each structure and the dielectric layer, and the structure and the dielectric layer can be used as an indirect risk evaluation influence factor. Therefore, the operation age (service life loss) of the power cable, the historical overheat fault condition, the load current, the surface temperature, the ambient temperature, the external wind speed, the conductor contact resistance and the contact area are selected as the influence factors of the temperature rise risk level of the single-core cable.
Step S202, normalizing the initial temperature rise risk data and the initial temperature rise risk training sample to obtain the temperature rise risk data of the cable terminal head to be tested and the temperature rise risk training sample of the cable database; the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal head and a corresponding sample class label;
step S203, extracting temperature rise risk influence factor data from the temperature rise risk data;
step S204, establishing a preliminary temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category labels;
in the embodiment of the invention, a preliminary temperature rise risk evaluation model is established according to the historical temperature rise risk data and the corresponding sample category label basis;
in a specific implementation, please refer to fig. 3, fig. 3 is a structural block diagram of a temperature rise risk evaluation model of the present invention, wherein 301 is an input value, 302 is an output value, and 303 is a neural unit;
setting the connection weight of the input layer to the hidden layer to w ig Setting the connection weight of the hidden layer to the output layer to w go Then, based on the input layer, the input gi to the mth neuron in the hidden layer is obtained g (m) and output go g (m) obtaining an input yi to the nth neuron in the output layer o (n) and output yo y (n), wherein the expression is specifically as follows:
wherein, w ig Connection weight value, w, for hidden layer go Is the connection weight value of the output layer, x i Being a neuron (i.e. a risk-influencing factor for temperature increase), gi g (m) is the input value for the mth neuron, go g (m) is the output value of the mth neuron, yi o (n) is an input value of the nth neuron, yo y (n) is the output value of the nth neuron, b g And b o Bias of neurons for the hidden layer, related to i, the bias being randomly acquired by the system, b g And b o The differences of (1) are mainly as follows: the former is the quantity used in the calculation between the input and hidden layers, and the latter is the quantity used in the calculation between the hidden layer and the output layer. In addition, the hidden layer is the middle layer in FIG. 3.
In the process, a sigmoid function can be selected as an activation function of a hidden layer and an output layer, and the weight between the layers is corrected by calculating the error of an output value.
The network structure input layer for determining the temperature rise risk assessment of the ABPNN single-core cable terminal comprises 8 neurons, which are respectively: operational Life span (loss of Life)x 1 Historical overheating fault conditionsx 2 Load currentx 3 Surface temperature ofx 4 Ambient temperaturex 5 External wind speedx 6 Conductor contact resistancex 7 Contact area of contactx 8 。
The output layer is the temperature rise risk grade of the switch cabinet, therefore, the neuron number of the output layer can be set to 3, namelyy 1 ，y 2 ，y 3 Each neuron output takes the value 0 or 1. Output "000" represents no risk, output "001" represents low risk, output "010" represents medium risk, and output "101" is high risk. The single core cable terminal temperature rise risk grade table is shown as follows:
step S205, training the preliminary temperature rise risk evaluation model through a genetic algorithm based on the historical temperature rise risk data and the corresponding sample category label to obtain a trained preliminary temperature rise risk evaluation model;
in an optional embodiment, based on the historical temperature rise risk data and the corresponding sample category label, training the preliminary temperature rise risk evaluation model through a genetic algorithm to obtain a trained preliminary temperature rise risk evaluation model, including:
initializing parameters of the preliminary temperature rise risk evaluation model;
inputting temperature rise risk influence factor data in the historical temperature rise risk data into the preliminary temperature rise risk evaluation model to generate a corresponding sample category;
determining a training error according to the historical temperature rise risk data, the corresponding sample class label and the sample class;
and adjusting the preliminary temperature rise risk evaluation model through the genetic algorithm based on the training error to obtain an optimal parameter, and optimizing the preliminary temperature rise risk evaluation model by adopting the optimal parameter to obtain the trained preliminary temperature rise risk evaluation model.
In the embodiment of the invention, the parameters of the preliminary temperature rise risk evaluation model are initialized, the temperature rise risk influencing factor data in the historical temperature rise risk data are input into the preliminary temperature rise risk evaluation model, the corresponding sample type is generated, the training error is determined according to the historical temperature rise risk data, the corresponding sample type label and the sample type, the preliminary temperature rise risk evaluation model is adjusted through a genetic algorithm based on the training error to obtain the optimal parameters, and the preliminary temperature rise risk evaluation model is optimized by adopting the optimal parameters to obtain the trained preliminary temperature rise risk evaluation model.
In the concrete implementation, (1) network parameters are set, and initial values of learning rate, weight and threshold are set;
(2) Initializing a population code, coding ABPNN initial network parameters to obtain a plurality of different individuals, determining the population quantity M and an initial population, and inputting the population quantity M and the initial population into a genetic algorithm;
(3) Calculating the fitness of the population and the fitness of the individual, wherein the calculation formula specifically comprises the following steps:
wherein N represents the number of training samples,
the output value is predicted for the sample(s),
the true output value for the sample.
(4) Initializing genetic algorithm parameters, and setting maximum iteration times, cross probability and mutation probability;
(5) Calculating the probability of an individual inheritance
And genetic cumulative probability
The calculation formula is specifically as follows:
(6) Carrying out crossing and variation of the population in a self-adaptive manner to obtain a new population, and calculating the fitness of the new population;
(7) And judging whether to terminate the iterative computation. The judging method comprises the following steps: and if the fitness of the new individual is smaller than the threshold value, outputting the individual, decoding, analyzing to obtain the ABPNN network optimal parameters, and otherwise, repeating the steps.
Step S206, verifying the trained preliminary temperature rise risk evaluation model based on the historical temperature rise risk data and the corresponding sample category labels to obtain a temperature rise risk evaluation model;
in the embodiment of the invention, the trained preliminary temperature rise risk evaluation model is verified according to the historical temperature rise risk data and the corresponding sample class labels, so that the temperature rise risk evaluation model is obtained.
Step S207, inputting the temperature rise risk influence factor data into the temperature rise risk evaluation model, and calculating to obtain temperature rise risk grade data of the cable terminal to be tested;
step S208, comparing the temperature rise risk grade data with a preset temperature rise threshold value to obtain temperature rise risk evaluation result data of the cable terminal to be tested;
in the embodiment of the invention, a preset temperature rise threshold value is set as a judgment standard, and the temperature rise risk grade data and the preset temperature rise threshold value are compared to obtain the temperature rise risk evaluation result data of the cable terminal to be tested.
In specific implementation, 100 groups of test sample data sets are acquired, four types of risks including no risk, low risk, medium risk and high risk are covered, a cable terminal head temperature rise risk evaluation test is performed based on the temperature rise risk evaluation model, and the calculation results are shown in the following table:
as can be seen from the above table, the average prediction accuracy of the temperature rise risk evaluation result is about 90%, and according to investigation and analysis of the prior art, there is no other temperature rise risk evaluation method for a single-core cable terminal, which can achieve the dual effects of high training efficiency and accuracy and strong capability of adaptively adjusting grid parameters.
According to the temperature rise evaluation method for the cable terminal, provided by the embodiment of the invention, the temperature rise risk data of the cable terminal to be evaluated and the temperature rise risk training sample of the cable database are obtained, the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal and a corresponding sample type label, temperature rise risk influence factor data are extracted from the temperature rise risk data, a temperature rise risk evaluation model is established according to the historical temperature rise risk data and the corresponding sample type label through a genetic algorithm, the temperature rise risk influence factor data are input into the temperature rise risk evaluation model, the temperature rise risk grade data of the cable terminal to be evaluated are obtained through calculation, the temperature rise risk evaluation result data of the cable terminal to be evaluated are obtained by comparing the temperature rise risk grade data with a preset temperature rise threshold value, the technical problem that the existing cable terminal temperature rise evaluation method has the defects that convergence is slow and the temperature rise easily falls into local optimization due to the adoption of a BP neural network is solved, and the accuracy of the temperature rise evaluation of the cable terminal is improved.
Referring to fig. 4, fig. 4 is a block diagram of a structure of an embodiment of a temperature rise evaluation device of a cable terminal according to the present invention, including:
the acquiring module 401 is configured to acquire temperature rise risk data of the cable terminal to be detected and a temperature rise risk training sample of a cable database; the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal head and a corresponding sample class label;
an extracting module 402, configured to extract temperature rise risk influencing factor data from the temperature rise risk data;
the establishing module 403 is configured to establish a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category label through a genetic algorithm;
a calculating module 404, configured to input the temperature rise risk influencing factor data into the temperature rise risk evaluation model, and calculate to obtain temperature rise risk level data of the cable terminal to be tested;
and the comparison module 405 is configured to compare the temperature rise risk level data with a preset temperature rise threshold value to obtain temperature rise risk evaluation result data of the cable terminal to be tested.
In an optional embodiment, the obtaining module 401 includes:
the acquisition submodule is used for acquiring initial temperature rise risk data of the cable terminal to be detected and an initial temperature rise risk training sample of a cable database;
and the processing submodule is used for carrying out normalization processing on the initial temperature rise risk data and the initial temperature rise risk training sample to obtain the temperature rise risk data of the cable terminal head to be tested and the temperature rise risk training sample of the cable database.
In an optional embodiment, the establishing module 403 includes:
the establishing submodule is used for establishing a preliminary temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category labels;
the training submodule is used for training the preliminary temperature rise risk evaluation model through the genetic algorithm based on the historical temperature rise risk data and the corresponding sample category label to obtain a trained preliminary temperature rise risk evaluation model;
and the verification submodule is used for verifying the trained preliminary temperature rise risk evaluation model based on the historical temperature rise risk data and the corresponding sample category label to obtain the temperature rise risk evaluation model.
In an alternative embodiment, the training submodule includes:
the generating unit is used for inputting temperature rise risk influence factor data in the historical temperature rise risk data into the preliminary temperature rise risk evaluation model and generating a corresponding sample type;
the error unit is used for determining a training error according to the historical temperature rise risk data, the corresponding sample class label and the sample class;
and the optimization unit is used for adjusting the preliminary temperature rise risk evaluation model through the genetic algorithm based on the training error to obtain an optimal parameter, and optimizing the preliminary temperature rise risk evaluation model by adopting the optimal parameter to obtain the trained preliminary temperature rise risk evaluation model.
In an optional embodiment, the training submodule further comprises:
and the initial unit is used for initializing parameters of the preliminary temperature rise risk evaluation model.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the method and apparatus disclosed in the present invention can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention, which is substantially or partly contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a readable storage medium and includes several instructions, so as to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A temperature rise evaluation method of a cable terminal is characterized by comprising the following steps:
acquiring temperature rise risk data of a cable terminal to be tested and a temperature rise risk training sample of a cable database; the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal and a corresponding sample category label;
extracting temperature rise risk influence factor data from the temperature rise risk data;
establishing a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category labels through a genetic algorithm;
inputting the temperature rise risk influence factor data into the temperature rise risk evaluation model, and calculating to obtain temperature rise risk grade data of the cable terminal to be tested;
and comparing the temperature rise risk grade data with a preset temperature rise threshold value to obtain the temperature rise risk evaluation result data of the cable terminal head to be tested.
2. The method for evaluating temperature rise of a cable termination according to claim 1, wherein obtaining temperature rise risk data of a cable termination to be tested and cable temperature rise risk training samples of a cable database comprises:
acquiring initial temperature rise risk data of a cable terminal to be tested and an initial temperature rise risk training sample of a cable database;
and normalizing the initial temperature rise risk data and the initial temperature rise risk training sample to obtain the temperature rise risk data of the cable terminal head to be tested and the temperature rise risk training sample of the cable database.
3. The method for evaluating temperature rise of a cable termination according to claim 1, wherein a temperature rise risk evaluation model is established by a genetic algorithm according to the historical temperature rise risk data and the corresponding sample category label, and the method comprises the following steps:
establishing a preliminary temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category label;
training the preliminary temperature rise risk evaluation model through the genetic algorithm based on the historical temperature rise risk data and the corresponding sample category label to obtain a trained preliminary temperature rise risk evaluation model;
and verifying the trained preliminary temperature rise risk evaluation model based on the historical temperature rise risk data and the corresponding sample category label to obtain the temperature rise risk evaluation model.
4. The method for evaluating temperature rise of a cable termination according to claim 3, wherein the step of training the preliminary temperature rise risk evaluation model based on the historical temperature rise risk data and the corresponding sample class labels by the genetic algorithm to obtain the trained preliminary temperature rise risk evaluation model comprises:
inputting temperature rise risk influence factor data in the historical temperature rise risk data into the preliminary temperature rise risk evaluation model to generate a corresponding sample category;
determining a training error according to the historical temperature rise risk data, the corresponding sample class label and the sample class;
and adjusting the preliminary temperature rise risk evaluation model through the genetic algorithm based on the training error to obtain an optimal parameter, and optimizing the preliminary temperature rise risk evaluation model by adopting the optimal parameter to obtain the trained preliminary temperature rise risk evaluation model.
5. The method for evaluating temperature rise of a cable termination according to claim 4, wherein the step of inputting the temperature rise risk influencing factor data in the historical temperature rise risk data into the preliminary temperature rise risk evaluation model and before generating the corresponding sample category further comprises:
and initializing parameters of the preliminary temperature rise risk evaluation model.
6. A temperature rise evaluation device of a cable terminal is characterized by comprising:
the acquisition module is used for acquiring temperature rise risk data of the cable terminal head to be detected and a temperature rise risk training sample of a cable database; the temperature rise risk training sample comprises historical temperature rise risk data of the cable terminal head and a corresponding sample class label;
the extraction module is used for extracting temperature rise risk influence factor data from the temperature rise risk data;
the establishing module is used for establishing a temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category label through a genetic algorithm;
the calculation module is used for inputting the temperature rise risk influence factor data into the temperature rise risk evaluation model and calculating to obtain temperature rise risk grade data of the cable terminal head to be detected;
and the comparison module is used for comparing the temperature rise risk grade data with a preset temperature rise threshold value to obtain temperature rise risk evaluation result data of the cable terminal head to be tested.
7. The apparatus for evaluating temperature rise of a cable termination according to claim 6, wherein the obtaining module comprises:
the acquisition submodule is used for acquiring initial temperature rise risk data of the cable terminal to be detected and an initial temperature rise risk training sample of a cable database;
and the processing submodule is used for carrying out normalization processing on the initial temperature rise risk data and the initial temperature rise risk training sample to obtain the temperature rise risk data of the cable terminal head to be tested and the temperature rise risk training sample of the cable database.
8. The apparatus for evaluating temperature rise of a cable termination according to claim 6, wherein the establishing module comprises:
the establishing submodule is used for establishing a preliminary temperature rise risk evaluation model according to the historical temperature rise risk data and the corresponding sample category labels;
the training submodule is used for training the preliminary temperature rise risk evaluation model through the genetic algorithm based on the historical temperature rise risk data and the corresponding sample category label to obtain a trained preliminary temperature rise risk evaluation model;
and the verification submodule is used for verifying the trained preliminary temperature rise risk evaluation model based on the historical temperature rise risk data and the corresponding sample category label to obtain the temperature rise risk evaluation model.
9. The temperature-rise evaluation device of a cable termination head according to claim 8, wherein the training submodule comprises:
the generating unit is used for inputting temperature rise risk influence factor data in the historical temperature rise risk data into the preliminary temperature rise risk evaluation model and generating a corresponding sample type;
the error unit is used for determining a training error according to the historical temperature rise risk data, the corresponding sample class label and the sample class;
and the optimizing unit is used for adjusting the preliminary temperature rise risk evaluation model through the genetic algorithm based on the training error to obtain an optimal parameter, and optimizing the preliminary temperature rise risk evaluation model by adopting the optimal parameter to obtain the trained preliminary temperature rise risk evaluation model.
10. The temperature-rise evaluation device of a cable termination head of claim 9, wherein the training submodule further comprises:
and the initial unit is used for initializing the parameters of the preliminary temperature rise risk evaluation model.
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| CN117040137A (en) * | 2023-10-09 | 2023-11-10 | 国网山东省电力公司聊城供电公司 | Ring main unit temperature rise early warning method, system, terminal and medium based on multi-source data |
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| CN103323703A (en) * | 2013-05-30 | 2013-09-25 | 武汉大学 | Cable connector fault diagnosing method |
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| CN103323703A (en) * | 2013-05-30 | 2013-09-25 | 武汉大学 | Cable connector fault diagnosing method |
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