CN115906639A - Method and device for predicting failure rate of line operation based on line operating conditions - Google Patents

Abstract

The invention relates to the technical field of line monitoring, in particular to a line operation fault rate prediction method and a device based on line operation working conditions, which comprises the steps of obtaining historical data of a plurality of line operation working conditions; classifying the historical data of the line operation condition by using a condition classifier; establishing a normal working condition line fault rate model and a severe working condition line fault rate model by using the proportional risk model and the classified line operation working condition historical data; inputting the operation condition data of the line to be predicted to a condition classifier, and determining the operation condition of the line to be predicted; and predicting the operation fault rate of the line to be predicted by using the line fault rate model corresponding to the working condition. According to the invention, the fault rate model of the normal working condition line and the fault rate model of the severe working condition line are established by utilizing the proportional risk model and the classified historical data of the line operating conditions, and the fault rate prediction is carried out on the lines under different working conditions by utilizing the line fault rate models under different working conditions in a targeted manner, so that the prediction accuracy is effectively improved.

Description

Line operation failure rate prediction method and device based on line operation conditions

Technical Field

The present invention relates to the technical field of line monitoring, in particular to a method and a device for predicting line operation failure rate based on line operation conditions.

Background Art

The distribution network is closely connected with users, and its operational safety will have a great impact on social order and people's lives. At present, the penetration rate of distributed renewable energy in the distribution network continues to increase, the load demand increases rapidly, and the distribution network structure becomes more and more complex. Accurate calculation of the distribution line failure rate is of great significance to the safe management of distribution network operation.

Distribution lines contain multiple components of various types. Changes in the state of a certain component of the line may have a chain effect, which will affect the failure rate of the line. In addition, its operating environment is complex and changeable, and various external factors may also cause the line operating state to change over time.

In actual projects, the scoring method is often used to determine the line status. The scoring method is based on DL/T 1249-2013 "Technical Guidelines for Overhead Transmission Line Operation Status Evaluation" and Q/GDW 173-2014 "Guidelines for Overhead Transmission Line Status Evaluation". The operating status of the components of the overhead transmission line is scored to evaluate the line operation failure probability. The scoring method is simple to operate and the conclusion is clear, but it relies on the experience of the evaluator and is highly subjective.

Summary of the invention

The present invention provides a method and device for predicting line operation failure rate based on line operation conditions, which overcomes the deficiencies of the above-mentioned prior art and can effectively solve the problems of strong subjectivity and low efficiency in the existing method of determining line status by manual scoring.

One of the technical solutions of the present invention is achieved by the following measures: a line operation failure rate prediction method based on line operation conditions, comprising:

Obtain historical data on the operating conditions of several lines;

Using a working condition classifier to classify the historical data of line operating conditions, where the classification types include normal working conditions and severe working conditions, where the working condition classifier is trained and learned using a line working condition sample set;

Using the proportional risk model and the classified historical data of line operation conditions, a normal condition line failure rate model and a severe condition line failure rate model are established.

Inputting the operating condition data of the line to be predicted into the operating condition classifier to determine the operating condition of the line to be predicted;

According to the classification results of the operating conditions of the line to be predicted, the line failure rate of the line to be predicted is predicted using the line failure rate model of the corresponding working condition.

The following are further optimizations and/or improvements to the above technical solutions:

The above uses the proportional risk model and the classified historical data of line operation conditions to establish a normal condition line failure rate model and a severe condition line failure rate model, including:

Based on the proportional risk model and combined with the influence of line load rate and meteorological factors, a line failure rate model is established;

Among them, β, γ ₁ , γ ₂ are parameters to be estimated; Z ₁ is the load factor; Z ₂ is the comprehensive impact of weather conditions; η is the expected life of the distribution line; t is the current time;

The line failure rate model is trained with the classified line operation condition history data to establish a normal condition line failure rate model and a severe condition line failure rate model as shown below;

The line failure rate model under normal operating conditions is:

Among them, β _a , γ _1a , and γ _2a are estimated parameters obtained by using the maximum likelihood estimation method based on historical data of line operation conditions under normal conditions;

The failure rate model of the line under severe working conditions is:

Among them, β _b , γ _1b , and γ _2b are estimated parameters obtained by using the maximum likelihood estimation method based on historical data of line operation conditions under severe conditions.

The above-mentioned establishment of the working condition classifier includes:

Integrate multi-source data of the distribution system, extract feature variables based on feature subsets based on correlation, establish an initial training sample set, and use the SMOTE algorithm to adjust the proportion of samples under normal and severe conditions to update the training sample set;

Establish a working condition classifier based on neural network;

The working condition classifier established based on the neural network is trained using the training sample set, and the trained working condition classifier is output.

The above also includes combining the result of predicting the line operation failure rate to be predicted by the line failure rate model of the corresponding working condition, and reversely adjusting the parameters in the normal working condition line failure rate model and the severe working condition line failure rate model.

The second technical solution of the present invention is achieved by the following measures: a line operation failure rate prediction device based on line operation conditions, comprising:

A data acquisition unit, which obtains historical data of operation conditions of several lines;

A classification unit, using a working condition classifier to classify the historical data of line operation conditions, wherein the classification types include normal working conditions and severe working conditions, wherein the working condition classifier is obtained by training and learning using a line working condition sample set;

The model building unit uses the proportional risk model and the classified historical data of line operation conditions to build a normal condition line failure rate model and a severe condition line failure rate model;

The operating condition identification unit inputs the operating condition data of the line to be predicted into the operating condition classifier to determine the operating condition of the line to be predicted;

The failure rate prediction unit predicts the operation failure rate of the line to be predicted based on the classification result of the operation condition of the line to be predicted and using the line failure rate model of the corresponding working condition.

The following are further optimizations and/or improvements to the above technical solutions:

The above taxa include:

The sample set establishment module integrates multi-source data of the distribution system, extracts feature variables based on feature subsets based on correlation, establishes the initial training sample set, and uses the SMOTE algorithm to adjust the proportion of samples under normal and severe working conditions, and updates the training sample set.

Classifier building module, building working condition classifier based on neural network;

The classifier training module uses the training sample set to train the working condition classifier established based on the neural network and outputs the trained working condition classifier.

The above-mentioned model building unit includes:

The first building module is to build a line failure rate model based on the proportional risk model and combined with the influence of line load rate and meteorological factors;

Among them, β, γ ₁ , γ ₂ are parameters to be estimated; Z ₁ is the load factor; Z ₂ is the comprehensive impact of weather conditions; η is the expected life of the distribution line; t is the current time;

The second establishment module trains the line failure rate model with the classified line operation condition history data, and establishes the normal condition line failure rate model and the severe condition line failure rate model as shown below;

The line failure rate model under normal operating conditions is:

Among them, β _a , γ _1a , and γ _2a are estimated parameters obtained by using the maximum likelihood estimation method based on historical data of line operation conditions under normal conditions;

The failure rate model of the line under severe working conditions is:

Among them, β _b , γ _1b , and γ _2b are estimated parameters obtained by using the maximum likelihood estimation method based on historical data of line operation conditions under severe conditions.

The present invention is suitable for short-term prediction of the operating status of distribution lines under non-extreme weather conditions. It uses a proportional risk model and classified historical data on line operating conditions to establish a normal operating condition line failure rate model and a severe operating condition line failure rate model. The line failure rate models under different operating conditions are used to perform targeted failure rate predictions on lines under different operating conditions, effectively improving the accuracy of the prediction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of the method of the present invention.

FIG2 is a flow chart of a method for establishing a normal operating condition line failure rate model and a severe operating condition line failure rate model in the present invention.

FIG3 is a schematic flow chart of the method for establishing an operating condition classifier in the present invention.

FIG4 is a flow chart of a method for implementing the SMOTE algorithm in the present invention.

FIG5 is a schematic diagram of the device structure of the present invention.

FIG6 is a schematic diagram of the structure of the classification unit in the present invention.

FIG. 7 is a schematic diagram of the structure of the model building unit in the present invention.

DETAILED DESCRIPTION

The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

The present invention will be further described below in conjunction with embodiments and accompanying drawings:

Embodiment 1: As shown in FIG. 1 , the embodiment of the present invention discloses a method for predicting line operation failure rate based on line operation conditions, comprising:

Step S101, obtaining historical data of operation conditions of several lines;

Step S102, using a working condition classifier to classify the historical data of line operating conditions, wherein the classification types include normal working conditions and severe working conditions, wherein the working condition classifier is obtained by training and learning using a line working condition sample set;

Step S103, using the proportional risk model and the classified historical data of line operation conditions, establish a normal operating condition line failure rate model and a severe operating condition line failure rate model;

Step S104, inputting the operating condition data of the line to be predicted into the operating condition classifier to determine the operating condition of the line to be predicted;

Step S105 , predicting the operation failure rate of the line to be predicted based on the classification result of the operation condition of the line to be predicted using the line failure rate model of the corresponding working condition.

The above also includes combining the prediction results of the line failure rate model of the corresponding working condition to predict the line operation failure rate, and reversely adjusting the parameters in the normal working condition line failure rate model and the severe working condition line failure rate model, so as to make the line failure rate prediction more accurate.

The present invention is suitable for short-term prediction of the operating status of distribution lines under non-extreme weather conditions. It uses a proportional risk model and classified historical data on line operating conditions to establish a normal operating condition line failure rate model and a severe operating condition line failure rate model. The line failure rate models under different operating conditions are used to perform targeted failure rate predictions on lines under different operating conditions, effectively improving the accuracy of the prediction.

Embodiment 2: As shown in FIG. 2 , the embodiment of the present invention discloses a method for predicting line operation failure rate based on line operation conditions, wherein a normal condition line failure rate model and a severe condition line failure rate model are established by using a proportional risk model and classified line operation condition historical data, further comprising:

Step S201, establishing a line failure rate model based on a proportional risk model and in combination with the influence of line load rate and meteorological factors;

Among them, β, γ ₁ , γ ₂ are parameters to be estimated; Z ₁ is the load factor; Z ₂ is the comprehensive impact of weather conditions; η is the expected life of the distribution line; t is the current time;

In this step, a line failure rate model is established, including:

(1) Establish the basic proportional hazard model (PHM model) shown in the following formula; PHM was proposed by D.R.Cox in 1972 and is widely used in the field of economics and failure modeling of mechanical components. In recent years, it has also been gradually applied to the reliability assessment of power transmission and transformation equipment in power systems.

Where t is the current moment; h ₀ (t) is the benchmark function; n is the dimension of historical data variables; γ _i is the covariate coefficient; and Z _i (t) is the covariate evaluation function.

(2) Under extreme weather conditions such as snowstorms and lightning strikes, municipal construction, and human damage, the failure rate of distribution lines will increase significantly, and the line failure rate model will evolve into a single factor model. Therefore, this embodiment selects the failure rate benchmark function as the distribution line aging Weibull distribution model for the transition probability of the line operation state under non-extreme weather conditions, as shown in the following formula:

Among them, β is the function shape parameter; η is the expected life of the distribution line. According to the reliability index of power transmission and distribution equipment in my country, the expected life of the distribution line is defined as a constant.

(3) For the short-term prediction of the failure rate of distribution lines, the change of equipment health status has little effect on the failure rate. Therefore, the influence of line load rate and meteorological factors is considered. Combining the above formula, the line failure rate model is established as shown in the following formula.

Among them, β, γ ₁ , γ ₂ are parameters to be estimated, which can be estimated based on the historical operation data of the distribution line; Z ₁ is the load rate, as shown below:

Z ₁ = l

Where l is the line load factor, which is given by the distribution line operation scheduling plan.

_Z2 is the comprehensive influence of weather conditions, as shown in the following formula:

Among them, λ _j is the weight corresponding to the jth meteorological factor x _j in the sample, which can be determined by solving the Pearson correlation coefficient based on historical statistical data; meteorological data can be obtained by querying the weather forecast.

Step S202, training a line failure rate model with the classified line operation condition history data, and establishing a normal condition line failure rate model and a severe condition line failure rate model as shown below;

The line failure rate model under normal operating conditions is:

Among them, β _a , γ _1a , and γ _2a are estimated parameters obtained by using the maximum likelihood estimation method based on historical data of line operation conditions under normal conditions;

The failure rate model of the line under severe working conditions is:

Among them, β _b , γ _1b , and γ _2b are estimated parameters obtained by using the maximum likelihood estimation method based on historical data of line operation conditions under severe conditions.

Embodiment 3: As shown in FIG. 3 , the embodiment of the present invention discloses a method for predicting line operation failure rate based on line operation conditions, wherein a condition classifier is established, further comprising:

Step S301, integrating multi-source data of the power distribution system, extracting feature variables by using feature subsets based on correlation, establishing an initial training sample set, and using the SMOTE algorithm to adjust the proportion of samples under normal working conditions and severe working conditions, and updating the training sample set;

The above-mentioned fusion of multi-source data of the distribution system includes ledger data, fault data, load rate data, weather data, etc. After the fusion of multi-source data of the distribution system, the data can be pre-processed, including cleaning and conversion, thereby greatly improving data integrity and availability.

Since the distribution line runs in a normal state for a much longer time than in a fault state, that is, the number of normal working condition samples in the initial sample is much larger than the number of bad working condition samples, which makes the initial sample significantly unbalanced. This imbalance will weaken the classification ability of the neural network model, thereby reducing the accuracy of the distribution line operation state prediction. Therefore, it is necessary to process the imbalance of the data. In this embodiment, the SMOTE algorithm is used to adjust the proportion of samples in normal working conditions and bad working conditions to improve the balance of the samples. The specific steps of the SMOTE algorithm are shown in Figure 4.

Step S302, establishing a working condition classifier based on a neural network;

The above specifically include:

A feedforward neural network structure is set up, and the signal propagates unidirectionally from the input layer to the output layer. Among them, the input layer is the m-dimensional feature vector _Xi that describes the line condition extracted based on the sample feature subset, and the output layer is the line condition classification result _Yi , that is, normal condition or severe condition. That is, the historical sample set composed of line condition feature variables is:

(X,y)＝(X ₁ ,y ₁ ),(X ₂ ,y ₂ ),...,(X _n ,y _n )y _i ∈{0,1}

The neural network information transmission process is as follows:

y＝g(WX+B)

Among them, W is the weight matrix of the line characteristic variables, B is the bias matrix, and g is the activation function.

The activation function of the hidden unit selects the RELU activation function and defines the vector α=WX+B, that is:

RELU(α _c )＝max(0,α _c )

Among them, α _c is the c-th element of vector α.

The weight matrix W and bias matrix B in the network are learned by the following gradient descent method, and the back propagation algorithm is used to achieve efficient calculation of gradients.

Among them, ε is the learning rate.

The optimization method of the training process adopts the stochastic gradient descent method (SGD) shown in the following formula.

▽L(θ)＝▽L(f(X ^(m) ; θ),y ^(m) )

The output unit is defined as a Sigmod unit, namely:

The Sigmod output unit uses the following cross entropy loss function.

为预测标签，M为初始训练集样本总数。Among them, y ^(m) is the true label,

is the predicted label, and M is the total number of samples in the initial training set.

Step S303: train the operating condition classifier established based on the neural network using the training sample set, and output the trained operating condition classifier.

In this embodiment, the accuracy of the operating condition neural network classifier is improved by extracting operating condition characteristic variables and improving sample balance, laying a foundation for the accuracy of prediction.

Embodiment 4: As shown in FIG. 5 , the embodiment of the present invention discloses a line operation failure rate prediction device based on line operation conditions, comprising:

A data acquisition unit, which obtains historical data of operation conditions of several lines;

A classification unit, using a working condition classifier to classify the historical data of line operation conditions, wherein the classification types include normal working conditions and severe working conditions, wherein the working condition classifier is obtained by training and learning using a line working condition sample set;

The model building unit uses the proportional risk model and the classified historical data of line operation conditions to build a normal condition line failure rate model and a severe condition line failure rate model;

The operating condition identification unit inputs the operating condition data of the line to be predicted into the operating condition classifier to determine the operating condition of the line to be predicted;

The failure rate prediction unit predicts the operation failure rate of the line to be predicted based on the classification result of the operation condition of the line to be predicted and using the line failure rate model of the corresponding working condition.

In this embodiment, as shown in FIG6 , the classification unit includes:

The sample set establishment module integrates multi-source data of the distribution system, extracts feature variables based on feature subsets based on correlation, establishes the initial training sample set, and uses the SMOTE algorithm to adjust the proportion of samples under normal and severe working conditions, and updates the training sample set.

Classifier building module, building working condition classifier based on neural network;

The classifier training module uses the training sample set to train the working condition classifier established based on the neural network and outputs the trained working condition classifier.

In this embodiment, as shown in FIG. 7 , the model building unit includes:

The first building module is to build a line failure rate model based on the proportional risk model and combined with the influence of line load rate and meteorological factors;

Among them, β, γ ₁ , γ ₂ are parameters to be estimated; Z ₁ is the load factor; Z ₂ is the comprehensive impact of weather conditions; η is the expected life of the distribution line; t is the current time;

The second establishment module trains the line failure rate model with the classified line operation condition history data, and establishes the normal condition line failure rate model and the severe condition line failure rate model as shown below;

The line failure rate model under normal operating conditions is:

Among them, β _a , γ _1a , and γ _2a are estimated parameters obtained by using the maximum likelihood estimation method based on historical data of line operation conditions under normal conditions;

The failure rate model of the line under severe working conditions is:

Among them, β _b , γ _1b , and γ _2b are estimated parameters obtained by using the maximum likelihood estimation method based on historical data of line operation conditions under severe conditions.

Embodiment 5: The embodiment of the present invention discloses a storage medium, on which a computer program readable by a computer is stored, and the computer program is configured to execute a method for identifying weak links in a power grid based on extreme ice disasters when running.

The above storage medium may include, but is not limited to: a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

Embodiment 6: The embodiment of the present invention discloses an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement a method for identifying weak links in a power grid based on extreme ice disasters.

The processor may be a central processing unit (CPU), a general purpose processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. It may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The memory may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory, a mobile hard disk, a magnetic disk or an optical disk.

Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

The above technical features constitute the best embodiment of the present invention, which has strong adaptability and best implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims (9)

1. A line operation fault rate prediction method based on line operation conditions is characterized by comprising the following steps:

obtaining historical data of a plurality of line operating conditions;

classifying historical data of the line operation condition by using a condition classifier, wherein the classification type comprises a normal condition and a severe condition, and the condition classifier is obtained by training and learning by using a line condition sample set;

establishing a normal working condition line fault rate model and a severe working condition line fault rate model by using the proportional risk model and the classified line operation working condition historical data;

inputting the operation condition data of the line to be predicted to a condition classifier, and determining the operation condition of the line to be predicted;

and predicting the operation fault rate of the line to be predicted by using the line fault rate model corresponding to the working condition according to the operation working condition classification result of the line to be predicted.

2. The line operation fault rate prediction method based on the line operation condition according to claim 1, wherein the establishing of the normal condition line fault rate model and the severe condition line fault rate model by using the proportional risk model and the classified line operation condition historical data comprises:

establishing a line fault rate model based on a proportional risk model and by combining the line load rate and the influence of meteorological factors;

wherein, beta, gamma ₁ 、γ ₂ Is a parameter to be estimated; z is a linear or branched member ₁ Is the load factor; z ₂ Is a composite effect of weather conditions; eta is the expected service life of the distribution line; t is the current time;

training the classified historical data of the line operating conditions on a line fault rate model, and establishing a normal condition line fault rate model and a severe condition line fault rate model as shown in the following;

the normal working condition line fault rate model is as follows:

wherein, beta _a 、γ _1a 、γ _2a Obtaining estimation parameters by adopting a maximum likelihood estimation method according to historical data of the line operation working conditions under normal working conditions;

the fault rate model of the line under the severe working condition is as follows:

wherein beta is _b 、γ _1b 、γ _2b And obtaining estimation parameters by adopting a maximum likelihood estimation method according to the historical data of the line operation working conditions under the severe working conditions.

3. The line operation fault rate prediction method based on the line operation condition according to claim 1 or 2, wherein the establishing of the condition classifier comprises:

multi-source data of a power distribution system are fused, feature subsets based on correlation degree are adopted to extract feature variables, an initial training sample set is established, SMOTE algorithm is adopted to adjust the sample number ratio of normal working conditions and severe working conditions, and the training sample set is updated;

establishing a working condition classifier based on a neural network;

and training the working condition classifier established based on the neural network by using the training sample set, and outputting the trained working condition classifier.

4. The line operation fault rate prediction method based on the line operation conditions according to any one of claims 1 to 3, characterized by further comprising the step of reversely adjusting parameters in the normal condition line fault rate model and the severe condition line fault rate model in combination with the result of predicting the line operation fault rate to be predicted by using the line fault rate model of the corresponding condition.

5. A line operation fault rate prediction device based on line operation conditions by applying the method of any one of claims 1 to 4, characterized by comprising:

the data acquisition unit is used for acquiring historical data of a plurality of line operating conditions;

the classification unit is used for classifying the historical data of the line operation working conditions by using a working condition classifier, wherein the classification type comprises normal working conditions and severe working conditions, and the working condition classifier is obtained by training and learning by using a line working condition sample set;

the model establishing unit is used for establishing a normal working condition line fault rate model and a severe working condition line fault rate model by utilizing the proportional risk model and the classified line operation working condition historical data;

the working condition identification unit inputs the operating working condition data of the line to be predicted to the working condition classifier and determines the operating working condition of the line to be predicted;

and the fault rate prediction unit is used for predicting the operation fault rate of the line to be predicted by using the line fault rate model corresponding to the working condition according to the operation working condition classification result of the line to be predicted.

6. The line operation failure rate prediction device according to claim 5, wherein the classification unit includes:

the system comprises a sample set establishing module, a correlation degree-based feature subset extraction feature variable, an initial training sample set, a SMOTE algorithm adjustment sample number ratio of normal working conditions and severe working conditions, and a training sample set updating module, wherein the sample set establishing module is used for fusing multi-source data of a power distribution system, extracting feature variables by adopting the feature subset based on the correlation degree, and updating the training sample set;

the classifier establishing module is used for establishing a working condition classifier based on a neural network;

and the classifier training module is used for training the working condition classifier established based on the neural network by utilizing the training sample set and outputting the trained working condition classifier.

7. The line operating condition-based line operating failure rate prediction device according to claim 5 or 6, wherein the model building unit includes:

the first establishing module is used for establishing a line fault rate model based on a proportional risk model and combined with the line load rate and the influence of meteorological factors;

wherein, beta, gamma ₁ 、γ ₂ Is a parameter to be estimated; z is a linear or branched member ₁ Is the load factor; z is a linear or branched member ₂ Is a composite effect of weather conditions; eta is the expected service life of the distribution line; t is the current time;

the second establishing module is used for training the line fault rate model by using the classified historical data of the line operating conditions, and establishing a normal condition line fault rate model and a severe condition line fault rate model as shown in the following;

the normal working condition line fault rate model is as follows:

wherein beta is _a 、γ _1a 、γ _2a Obtaining an estimation parameter by adopting a maximum likelihood estimation method according to the historical data of the line operating condition under the normal condition;

the fault rate model of the line under the severe working condition is as follows:

wherein, beta _b 、γ _1b 、γ _2b And obtaining estimation parameters by adopting a maximum likelihood estimation method according to the historical data of the line operation working conditions under the severe working conditions.

8. A storage medium having stored thereon a computer program readable by a computer, the computer program being arranged to execute the method of predicting line operation fault rate based on line operating conditions according to any one of claims 1 to 4 when running.

9. An electronic device, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded by the processor and executed to implement the method for predicting line operation fault rate according to any one of claims 1 to 4.

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