CN117031202A - K-SMOTE and depth forest based power transmission line fault multi-source diagnosis method and system - Google Patents
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Abstract
The invention discloses a power transmission line fault multi-source diagnosis method and system based on K-SMOTE and deep forests, relates to the field of power transmission line fault diagnosis, and aims at solving the problems of unbalance classification and low diagnosis accuracy of the existing fault data set. Firstly, taking historical transmission line fault information of a national network as a database, dividing the historical transmission line fault information into multiple ranges of data sets according to fault types, wherein each data set is composed of time-frequency domain characteristics of transmission line fault voltage current waveform signals; secondly, clustering a few fault data sets by adopting a K-means method, expanding the data sets by using an SMOTE oversampling method, and balancing various fault sub-databases; finally, using the balanced fault sub-data set, and carrying out model training based on a depth forest algorithm to obtain a trained fault diagnosis model; and diagnosing the faults by using the trained fault diagnosis model. The method has good accuracy and effectiveness, and provides diagnosis with high accuracy and high coverage rate for the power transmission line faults.
Description
Technical Field
The invention relates to the field of fault detection of power transmission and distribution lines of a power grid system, in particular to a multi-source fault diagnosis method and system for a power transmission line based on K-SMOTE and deep forests.
Background
With the increasing large, complicated and intelligent of the China power transmission and distribution system, the operation characteristics of the power grid are changed deeply, and the power grid dispatching also faces new significant challenges. On one hand, the power transmission and distribution system is influenced by factors such as changeable regional climate, complex operating environment and the like, and the synchronous control of the operating situation of the power grid is urgently needed; on the other hand, the power grid transmitting and receiving end and the AC/DC coupling are close, and global safety risks are easy to be caused.
Therefore, the diagnosis of the power grid faults is very important to assist the normal operation and the dispatch operation of the power grid. When the power grid fails, massive fault alarm data (comprising correct alarm information, error alarm information, repeated alarm information and irrelevant information) collected by the monitoring system are sent to the dispatching center from the local automatic device. The existing fault diagnosis method classifies the fault data set in an unbalanced way, and the fault diagnosis accuracy is low.
Disclosure of Invention
The invention provides a power transmission line fault multi-source diagnosis method and system based on K-SMOTE and deep forests, which are used for solving the technical problems of unbalanced classification of a power distribution line fault data set and low diagnosis accuracy in the prior art.
According to an aspect of the specification, a power transmission line fault multi-source diagnosis method based on K-SMOTE and depth forests is provided, and the method comprises the following steps:
acquiring fault data of a power transmission line;
inputting the power transmission line fault data into a trained fault diagnosis model to obtain a fault diagnosis result;
the training of the fault diagnosis model comprises the following steps:
extracting time-frequency characteristics of voltage and current waveforms in historical fault data, and performing fault classification according to the extracted time-frequency characteristics to form fault waveform data sets of different fault types;
clustering the unbalanced data set in the fault data set by using a K-means clustering algorithm, and performing data expansion on the clustered unbalanced data set by using SMOTE oversampling;
forming a failure sub-data set according to the balanced data set and the expanded unbalanced data set;
and performing model training by using the fault sub-data set and based on a depth forest algorithm to obtain a trained fault diagnosis model.
Preferably, the extracted time-frequency characteristics comprise transient waveform characteristics of a time domain, a frequency domain and a time-frequency domain.
Preferably, the K-means clustering algorithm is used to cluster unbalanced data sets in the fault data sets, and the method further comprises:
step one: for the dataset, extracting k initial cluster center points as i 1 ，i 2 ，…，i k ；
Step two: for other data except the clustering center in the data set, the data is obtained through a formula c i ＝argmin[|x i -i j |] 2 Calculate each data and i j (j=1, 2, …, k), and classifying the data nearest to i, j into one type to obtain k types of data;
step three: calculating the average value of data in various types, setting the calculated average value as the central value of the type, and calculating the sum of Euclidean distances from each data point to the central value by using the formula in the second step, wherein the sum is calculated as S;
step four: and repeating the second and third steps until the result S is unchanged, and outputting a K-means cluster subset.
Preferably, the data expansion of the clustered unbalanced data set by utilizing SMOTE oversampling includes:
step one: calculating the distance from each sample x of a minority class in the unbalanced data set to all samples in the minority class sample set by taking Euclidean distance as a standard to obtain k nearest neighbor;
step two: determining a sampling multiplying power N, and randomly selecting a plurality of samples from k neighbors of each minority sample x;
step three: randomly selecting a neighbor Xnew, wherein the neighbor Xnew and the original sample pass through the formulaConstructing a new sample; rand (0, N) is a random number generated between 0 and N, +.>Is the cluster center, x is the sample of this type.
Preferably, the depth forest algorithm adopts a depth forest multi-granularity cascade forest model.
According to still another aspect of the present disclosure, there is provided a K-SMOTE and depth forest based transmission line fault multi-source diagnosis system, the system including:
an acquisition unit: acquiring fault data;
diagnosis unit: inputting the power transmission line fault data into a trained fault diagnosis model to obtain a fault diagnosis result;
training unit: extracting time-frequency characteristics of voltage and current waveforms in historical fault data, and carrying out fault classification according to the extracted time-frequency characteristics to form fault data sets of different fault types;
clustering the unbalanced data set in the fault data set by using a K-means clustering algorithm, and performing data expansion on the clustered unbalanced data set by using SMOTE oversampling;
forming a failure sub-data set according to the balanced data set and the expanded unbalanced data set;
and performing model training by using the fault sub-data set and based on a depth forest algorithm to obtain a trained fault diagnosis model.
According to a further aspect of the present description, there is provided an electronic device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the K-SMOTE and depth forest based transmission line fault multisource diagnostic method.
Based on a further aspect of the present description, a computer readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the K-SMOTE and depth forest based transmission line fault multisource diagnosis method.
Compared with the prior art, the invention has the beneficial effects that:
according to the K-SMOTE and depth forest based transmission line fault multisource diagnosis method and system, the characteristics of the transmission line faults are extracted and classified, and the K-means cluster analysis is carried out to correct the unbalanced classification subset, so that the problem that noise data points are reinforced by an SMOTE algorithm during small sample processing due to complex fault data classification conditions and unbalanced data depth is avoided, and the accuracy and coverage rate of subsequent depth forest based training are improved.
Drawings
Fig. 1 is a flowchart of a transmission line fault multi-source diagnosis method based on K-SMOTE and depth forests according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a K-SMOTE and depth forest based transmission line fault multisource diagnostic system according to an embodiment of the present invention;
FIG. 3 is a flowchart of a training process for a fault diagnosis model according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully hereinafter with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1 and fig. 3, the present embodiment provides a power transmission line fault multi-source diagnosis method based on K-SMOTE and depth forests, including:
acquiring fault data of a power transmission line;
inputting the power transmission line fault data into a trained fault diagnosis model to obtain a fault diagnosis result;
the training of the fault diagnosis model comprises the following steps:
extracting time-frequency characteristics of voltage and current waveforms in historical fault data, and performing fault classification according to the extracted time-frequency characteristics to form fault waveform data sets of different fault types;
clustering the unbalanced data set in the fault waveform data set by using a K-means clustering algorithm, and performing data expansion on the clustered unbalanced data set by using SMOTE oversampling;
forming a failure sub-data set according to the balanced data set and the expanded unbalanced data set;
and performing model training by using the fault sub-data set and based on a depth forest algorithm to obtain a trained fault diagnosis model.
Specifically, the waveform characteristics extracted by the historical fault waveform database are transient waveform characteristics in three aspects of time domain, frequency domain and time-frequency domain. The method comprises the steps of extracting transient waveform time domain characteristics, including waveform mean value, mean square error, square root amplitude, peak value, skewness, peak value factor and waveform factor time domain characteristic parameters, extracting transient frequency domain, including center of gravity frequency, average frequency, frequency standard deviation, root mean square frequency and frequency domain spectrum power, and extracting transient time-frequency domain, including wavelet entropy;
meanwhile, the formula is adopted for normalization treatment so as to eliminate the inconsistency of the dimension, and the formula is as follows:in the formula, y i Is normalized data; x is x max 、x min Maximum and minimum values in the extracted characteristic parameters;
and performing fault classification on the time-frequency characteristics, wherein the fault classification comprises the following steps: the lightning stroke non-lightning stroke fault two-classification, the lightning stroke multi-classification or the overall multi-classification, thereby constructing fault waveform data sets of different types;
specifically, the classified data sets have different data quantity of each class, the class with the largest data quantity is used as a balanced data set, the data sets of other remaining classes are all unbalanced data sets, and the remaining data sets are clustered and oversampled;
based on historical fault information of the national network, a fault waveform data set is initially established, wherein the total number of data is 3415, the total number of lightning stroke data is 994, and the total number of non-lightning stroke data is 2421; after removing the data without specific shielding failure counterattack type, remaining lightning stroke data 582 strips, wherein 485 strips are shielded and 97 strips are counterattack; after the non-lightning stroke data without the thin type tag are removed, 898 pieces of non-lightning stroke data are remained, wherein 120 pieces of bird damage, 162 pieces of ice damage, 186 pieces of windage yaw, 365 pieces of external damage and 65 pieces of other data.
In particular, the unbalanced data sets are clustered by a K-means clustering algorithm, including,
step one: for the dataset, extracting k initial cluster center points as i 1 ，i 2 ，…，i k ；
Step two: for other data of the data set except the clustering center, the data is obtained through the formula c i ＝argmin[|x i -i j |] 2 Calculate each data and i j (j=1, 2, …, k) and classifying the data nearest to i, j into a class, realizing the classification of the data into k classes;
step three: calculating the average value of data in various types, setting the calculated average value as the central value of the type, and calculating the sum of Euclidean distances from each data point to the central value by using the formula in the second step, wherein the sum is calculated as S;
step four: and repeating the second and third steps until the result S is unchanged, and outputting a K-means cluster subset.
11. Specifically, the method for performing data expansion on the clustered unbalanced data set by utilizing SMOTE oversampling further comprises the following steps:
step one: for each sample x of a minority class in the unbalanced data set, calculating the distance from the sample x to all samples in the minority class sample set by taking Euclidean distance as a standard to obtain k nearest neighbor;
step two: determining a sampling multiplying power N, and randomly selecting a plurality of samples from k neighbors of each minority sample x;
step three: randomly selecting a neighbor Xnew, wherein the neighbor Xnew and the original sample pass through the formulaConstructing a new sample; rand (0, N) is a random number generated between 0 and N, +.>Is the cluster center, x is the sample of this type.
The over-sampling requirement of the SMOTE is that the data quantity of the other categories is increased until the data quantity of the other categories is consistent with the most data type except the most data type; through SMOTE oversampling, interpolation is carried out on the connection line between the clustering center of each data set and other sample points, the clustering samples are corrected, the data sets are expanded, and a fault sub-data set is constructed;
specifically, the depth forest algorithm adopts a depth forest multi-granularity cascade forest model, which comprises,
the multi-granularity cascade forest model comprises multi-granularity scanning and cascade forests; the multi-granularity scanning part screens the characteristics and generates characteristic quantities which have closer relation with classification. Firstly, original data with the dimension K is scanned by utilizing a sliding window with the length L, each sliding step length is S, and N L-dimensional feature sub-vectors are obtained after sliding sampling is finished, wherein N= (K-L)/S+1. Assuming that the original data is distinguished by 10 fault feature classes, the input samples are 300-dimensional vectors, the granularity of 100 is used for scanning, the sliding step length is 1 each time, a total of 201 100-dimensional vectors can be generated, each vector generates 1 10-dimensional class vector through a random forest, and the total generates 2010-dimensional input vectors;
the multi-granularity cascade forest model takes random forests as a basic learner to carry out integrated learning, wherein each forest of cascade forests is composed of a plurality of decision trees, and the decision trees adopt a classification and regression tree (CAET) algorithm;
wherein Pm represents the proportion of the m-th sample in the current sample set D, y is a non-zero integer, gini (D) is a radix index, the smaller the value of the value is, the higher the purity of the data set D is, when the CART node is constructed, the radix coefficient of the current feature of the current training sample set under all possible values is calculated, and the feature corresponding to the minimum radix coefficient and the splitting point corresponding to the minimum radix coefficient are selected as the splitting condition of the current node;
the cascading forests are used for processing the sample characteristics layer by layer, so that the accuracy of the algorithm pattern recognition is enhanced. Each layer is provided with 1 random forest and 1 completely random forest, the input data of the 1 st layer of the cascade forests are 4020-dimensional vectors spliced by 2010-dimensional vectors of the random forests and 2010-dimensional vectors of the completely random forests, and 2 two-dimensional class vectors are obtained after classification processing; then splicing the 2 two-dimensional category vectors with 4020-dimensional initial feature vectors to form a 4024-dimensional new feature vector serving as input of a second layer; and so on according to the method. And finally, averaging the class vectors output by the Nth layer, and selecting the class corresponding to the maximum value as the final classification result of the fault.
In this embodiment, after the fault waveform data sets of different types are processed, the number of data in the minority class data set is increased, the number of data in the training set is increased, and the depth forest is used as a classification method for classification, so that the accuracy of the classification result is improved. The processed data were classified using the Weka test, the non-lightning multi-classification accuracy was 71.27% with a training set and test set of 9:1, the highest accuracy after 39 feature SMOTE processing was 77.90%,
in this embodiment, the ratio of the training set to the testing set of the depth forest algorithm is 7:3, a step of; the K-means-SMTE is combined with a depth forest algorithm, and the accuracy evaluation index is accuracy precision=TP/(TP/TP+FP), namely the correct prediction is positive accounting for the positive proportion of all predictions; the K-means-SMOTE is combined with a depth forest algorithm according to the Weka simulation result, and the lightning stroke/non-lightning stroke classification precision can reach 90%; the lightning stroke shielding failure/counterattack classification precision can reach 86%; the non-lightning multi-classification precision can reach 73%; the overall multi-classification precision can reach 79%, and the precision is improved by more than 10% compared with the traditional random forest method and the like.
As shown in fig. 2 and fig. 3, this embodiment further provides a power transmission line fault multisource diagnosis system based on K-SMOTE and depth forests, including:
an acquisition unit: acquiring fault data;
diagnosis unit: inputting the power transmission line fault data into a trained fault diagnosis model to obtain a fault diagnosis result;
the training of the fault diagnosis model comprises the following steps:
extracting time-frequency characteristics of voltage and current waveforms in historical fault data, and carrying out fault classification according to the extracted time-frequency characteristics to form fault data sets of different fault types;
clustering the unbalanced data set in the fault data set by using a K-means clustering algorithm, and performing data expansion on the clustered unbalanced data set by using SMOTE oversampling;
forming a failure sub-data set according to the balanced data set and the expanded unbalanced data set;
and performing model training by using the new fault data set and based on a depth forest algorithm to obtain a trained fault diagnosis model.
The embodiment also provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program realizes the steps of the power transmission line fault multi-source diagnosis method based on the K-SMOTE and the depth forest when being executed by a processor.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media Card (smart media Card), a Secure Digital Card (Flash Card), or the like, which is provided on the computer device.
The embodiment also provides an electronic device, which comprises a processor, a memory and a computer program stored on the memory and executable by the processor, wherein the steps of the K-SMOTE and depth forest-based transmission line fault multi-source diagnosis method are realized when the computer program is executed by the processor.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The power transmission line fault multi-source diagnosis method based on the K-SMOTE and the depth forest is characterized by comprising the following steps of:
acquiring fault data of a power transmission line;
inputting the power transmission line fault data into a trained fault diagnosis model to obtain a fault diagnosis result;
the training of the fault diagnosis model comprises the following steps:
extracting time-frequency characteristics of voltage and current waveforms in historical fault data, and performing fault classification according to the extracted time-frequency characteristics to form fault waveform data sets of different fault types;
clustering the unbalanced data set in the fault waveform data set by using a K-means clustering algorithm, and performing data expansion on the clustered unbalanced data set by using SMOTE oversampling;
forming a failure sub-data set according to the balanced data set and the expanded unbalanced data set;
and performing model training by using the fault sub-data set and based on a depth forest algorithm to obtain a trained fault diagnosis model.
2. The K-SMOTE and depth forest based transmission line fault multisource diagnosis method according to claim 1, wherein the extracted time-frequency features include time domain, frequency domain and time-frequency domain transient waveform features.
3. The K-SMOTE and depth forest based transmission line fault multisource diagnosis method according to claim 1, wherein the fault classification mode comprises: lightning stroke non-lightning stroke fault two-classification, lightning stroke multi-classification, non-lightning stroke multi-classification or overall multi-classification.
4. The K-SMOTE and deep forest based transmission line fault multisource diagnosis method according to claim 1, wherein the fault waveform data set comprises a balanced data set and an unbalanced data set, the balanced data set is the data set with the largest number of fault data, and the rest data sets are all unbalanced data sets.
5. The K-SMOTE and depth forest based transmission line fault multisource diagnosis method according to claim 1, wherein the K-means clustering algorithm is used to cluster unbalanced data sets in the fault data set, further comprising:
step one: for the dataset, extracting k initial cluster center points as i 1 ，i 2 ，…，i k；
Step two: for other data except the clustering center in the data set, the data is obtained through a formula c i ＝argmin[|x i -i j |] 2 Calculate each data and i j (j=1, 2, …, k), and classifying the data nearest to i, j into one type to obtain k types of data;
step three: calculating the average value of data in various types, setting the calculated average value as the central value of the type, and calculating the sum of Euclidean distances from each data point to the central value by using the formula in the second step, and recording as S;
step four: and repeating the second and third steps until the result S is unchanged, and outputting a K-means cluster subset.
6. The K-SMOTE and depth forest based transmission line fault multisource diagnostic method of claim 1, wherein the data expansion of the clustered unbalanced data set using SMOTE oversampling further comprises:
step one: for each sample x of a minority class in the unbalanced data set, calculating the distance from the sample x to all samples in the minority class sample set by taking Euclidean distance as a standard to obtain k nearest neighbor;
step two: determining a sampling multiplying power N, and randomly selecting a plurality of samples from k neighbors of each minority sample x;
step (a)Thirdly,: randomly selecting a neighbor Xnew, wherein the neighbor Xnew and the original sample pass through the formulaConstructing a new sample; rand (0, N) is a random number generated between 0 and N, +.>Is the cluster center, x is the sample of this type.
7. The K-SMOTE and depth forest based transmission line fault multisource diagnosis method according to claim 1, wherein the depth forest algorithm adopts a depth forest multiscale cascade forest model.
8. The power transmission line fault multi-source diagnosis system based on K-SMOTE and deep forest is characterized in that the system comprises,
an acquisition unit: acquiring fault data;
diagnosis unit: inputting the power transmission line fault data into a trained fault diagnosis model to obtain a fault diagnosis result;
the training of the fault diagnosis model comprises the following steps:
extracting time-frequency characteristics of voltage and current waveforms in historical fault data, and carrying out fault classification according to the extracted time-frequency characteristics to form fault data sets of different fault types;
clustering the unbalanced data set in the fault data set by using a K-means clustering algorithm, and performing data expansion on the clustered unbalanced data set by using SMOTE oversampling;
forming a failure sub-data set according to the balanced data set and the expanded unbalanced data set;
and performing model training by using the fault sub-data set and based on a depth forest algorithm to obtain a trained fault diagnosis model.
9. An electronic device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the steps of the K-SMOTE and depth forest based transmission line fault multisource diagnostic method according to any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the K-SMOTE and depth forest based transmission line fault multisource diagnostic method according to any one of claims 1 to 7.
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