CN110689324A - Auxiliary judging method for detection result of distribution transformer - Google Patents
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Abstract
The invention provides an auxiliary judging method of a distribution transformer detection result, S1, sorting and extracting qualified distribution transformer sampling detection data, and sorting the qualified distribution transformer sampling detection data into a data characteristic sample set; s2, performing data cleaning on the data characteristic sample set; s3, carrying out normalization processing on the data characteristic quantity by using a Z-score standardization method; s4, establishing a single-class support vector machine judgment model to obtain a judgment model parameter; and S5, after training is finished, judging by using the established judgment model according to the test data set, marking abnormal points and outputting results. The method comprises the steps of preprocessing detection data of all indexes of a plurality of detection reports, training normal data by using an OCSVM (online closed system virtual machine) to obtain reasonable model parameters, and distinguishing a test set by using a trained model, so that an abnormal detection report suspected of being wrongly evaluated can be found out. And an experimenter can refer to the evaluation result to determine whether to perform retest on the transformer corresponding to the abnormal detection report.
Description
Technical Field
The invention belongs to the field of material detection of power systems, and provides an auxiliary judgment method for a detection result of a distribution transformer.
Background
The power grid material detection is the key work of putting good power grid materials into a gateway and ensuring the intrinsic safety of a power grid. With the advance of the upgrading and transforming work of distribution networks and rural networks, the coverage and working requirements of power grid material detection are constantly improved, the detection traffic volume is increasing day by day, the existing material quality detection system has some defects in the aspects of bearing capacity, working efficiency, resource utilization, deep analysis and the like, can not adapt to the practical needs of rapid development of power grids and intensive material resource management, and needs to be improved in technology and management ways urgently.
The 10kV distribution transformer is a main device in a distribution network, and has a plurality of detection indexes and procedures in the detection process. In the actual detection process, due to special conditions such as errors of a measuring instrument, negligence of inspection workers and the like, unqualified transformer products can be judged to be qualified products, so that the unqualified products flow into a power grid, and adverse consequences are caused. In order to avoid the consequence as much as possible, in the detection process, if some 10kV transformers are found to have partial indexes higher or close to the qualified limit value, but are within the qualified range; in this case, the inspector may determine whether or not to recheck the transformer based on experience and intuition.
Disclosure of Invention
In view of the above problems, the present invention provides an auxiliary evaluation method for a detection result of a distribution transformer, which is used for auxiliary evaluation of the detection result of the distribution transformer. The method and the device can find out the suspected misjudged detection report for the reference of detection personnel so as to determine whether to perform the retest on the transformer corresponding to the suspected misjudged detection report.
The technical scheme of the invention is as follows:
an auxiliary evaluation method for a detection result of a distribution transformer comprises the following specific steps:
s1, sorting and extracting qualified sampling detection data of distribution network transformers, classifying the obtained data according to transformer capacity, sorting the transformer data with the capacity of 10kV, which accounts for the highest proportion of the data, into a data characteristic sample set, regarding the transformer as a sample, and regarding data generated by various tests performed on the transformer as the characteristic attribute of the sample;
s2, performing data cleaning on the data characteristic sample set, removing blank or repeated detection data, and converting the data characteristic sample set subjected to data cleaning into a vector data set;
s3, carrying out normalization processing on the data characteristic quantity by using a Z-score normalization method, wherein the processed data conform to standard normal distribution, namely the mean value is 0, the standard deviation is 1, and the conversion function is as follows:
in the formula, x*Is the value of Z, x is the normalized random variable, u is the sample mean, σ is the sample standard deviation;
s4, establishing a single-class support vector machine judgment model, and training the data set subjected to normalization processing by using the judgment model to obtain a judgment model parameter;
and S5, after training is finished, judging by using the established judgment model according to the test data set, marking abnormal points, outputting results, and verifying the effect of auxiliary judgment of the judgment model.
The detection data in the step S1 include the dc resistance, the short-circuit resistance, the ground resistance, the unbalance rate, the no-load loss experimental data, the short-circuit loss experimental data, the induced withstand voltage and the applied withstand voltage experimental data, and the temperature rise experimental data of the transformer, and can relatively comprehensively reflect the quality level of the transformer.
In step S4, a single-type support vector machine model is directly established by using a python development platform of a skearn platform according to the following statements:
OCSVM(nu＝,kernel＝,gamma＝)
wherein nu represents the number of abnormal points in the training set, and the value range is [0, 1 ]; kernel represents the selected kernel function, and various kernel functions of 'rbf', 'poly' and 'sigmoid' are available; gamma is a parameter of the 'rbf', 'poly' or 'sigmoid' kernel function for controlling the continuity of the boundary between the normal point and the abnormal point, the lower the gamma, the higher the continuity of the boundary, the more likely the continuity is to surround all the normal data points with one hypersphere, and vice versa, the more likely the hypersphere is to surround the normal data points respectively, but overfitting is easy to occur.
The test data set in step S5 is a artificially constructed test set in which the abnormal points are distributed.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of preprocessing detection data of all indexes of a plurality of detection reports, training normal data by using an OCSVM (online closed system virtual machine) to obtain reasonable model parameters, and distinguishing a test set by using a trained model, so that an abnormal detection report suspected of being wrongly evaluated can be found out. And an experimenter can refer to the evaluation result to determine whether to perform retest on the transformer corresponding to the abnormal detection report.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a graph of the distance of each data point of the test data set from the boundary hypersphere.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
According to the statistical probability, the detection results close to most of the detection results are normal and credible, and although each item of detection data of few detection results meets the standard, the value distribution and the proportional relation between the values are different from most of the normal detection results, so that the detection results are considered to be unreliable and possibly unqualified products which are misjudged to be normal products. Moreover, the distribution of such unreliable detection results in the data space should be in a region where the data points are sparse, most of the data is normal data, and in the feature space, the normal data and abnormal data occupy different spatial regions. If the area occupied by normal data in the feature space can be estimated, points not in the area can be considered as abnormal points, and the normal data and the abnormal data are separated by a hyperplane by using a single-class Support vector machine (OCSVM) and compressing the area occupied by the data set in the feature space by maximizing the classification boundary.
Aiming at the auxiliary judgment requirement of the detection result of the power distribution transformer of the power grid, the invention takes the qualified detection result (positive sample) of the sampling of the 10kV transformer of a certain power-saving network as a research object and adopts an OCSVM algorithm to evaluate the detection result. The method comprises the steps of preprocessing detection data of all indexes of a plurality of detection reports, training normal data by using an OCSVM (online closed system virtual machine) to obtain reasonable model parameters, and distinguishing a test set by using a trained model, so that an abnormal detection report suspected of being wrongly evaluated can be found out.
OCSVM-based detection result auxiliary judgment method for abnormal point identification
Anomaly detection is an important component in data mining. Anomalous data refers to small portions of data in a data set that deviate from a large portion of data or are not subject to the same statistical model as other large portions of data in the data set. And the anomaly detection is to identify anomalous data. Anomaly detection machine learning algorithms can be divided into 2 major categories: supervised learning algorithms and unsupervised learning algorithms. The OCSVM algorithm detection result in the unsupervised learning algorithm is used for judging.
The principle is that after data cleaning is carried out on detection data (positive samples) of a batch of normal distribution network transformer detection reports, normalization processing is carried out, and then an OCSVM is used for training a normal report data set to obtain reasonable model parameters. After training, the model is used for testing the detection report set to be tested, so that abnormal points, namely detection reports suspected of misjudgment, are obtained.
OCSVM principle
The OCSVM is a refinement of a two-classification support vector machine, and is a novel machine learning algorithm developed on the basis of a statistical learning theory. The OCSVM adopts a structure minimization principle, has minimized risk experience and confidence range, has the advantages of high fitting precision, few selected parameters, strong popularization capability, global optimization and the like, and belongs to an important classical small sample learning algorithm in the field of anomaly detection. The detection of anomalous data by the OCSVM may provide good generalization capability when determining the appropriate parameter configuration.
OCSVM utilizes a sample set { xi,i＝1,2,..,l},xi∈RnAnd mapping the non-linear kernel function phi (x) in a high-dimensional feature space H, and calculating a minimum hypersphere containing as many samples as possible as a decision boundary. Wherein a is the center of the hyper-sphere and R is the radius of the hyper-sphere. The solution of the minimum hypersphere containing all training samples can be transformed into the following convex optimization problem:
where C is a penalty factor, ξi> 0 is a relaxation variable. The inner product operation in the high-dimensional space optimization is replaced by a kernel function meeting the conditions of the mercer, namely, a kernel function is found so that:
K(x,y)＝<Φ(x),Φ(y)>(2)
the dual problem of equation (1) can be expressed as:
the lagrange method can be used for solving the problem formula (3) to obtain aiOnly a small part aiAnd the corresponding point is a support vector when the value is more than or equal to 0. The center a of the hyper-sphere can be represented by
The radius R can be determined by substituting any support vector into the following equation:
the final form of the decision function is:
for a given x, a normal point is determined when f (x) > 0 and an outlier is determined when f (x) ≦ 0.
Referring to fig. 1, the present invention provides a technical solution:
an auxiliary evaluation method for distribution network transformer inspection results comprises the following specific steps:
s1, sorting and extracting qualified sampling detection data of the distribution transformer, classifying the obtained data according to transformer capacity, sorting the transformer data with the capacity of 10kV, which accounts for the highest proportion of the data, into a data characteristic sample set, regarding the transformer as a sample, and regarding data generated by various tests performed on the transformer as the characteristic attribute of the sample;
s2, performing data cleaning on the data characteristic sample set, removing blank or repeated detection data, and converting the data characteristic sample set subjected to data cleaning into a vector data set;
s3, carrying out normalization processing on the data characteristic quantity by using a Z-score normalization method, wherein the processed data conform to standard normal distribution, namely the mean value is 0, the standard deviation is 1, and the conversion function is as follows:
in the formula, x*Is the value of Z, x is the normalized random variable, u is the sample mean, σ is the sample standard deviation;
s4, establishing a single-class support vector machine judgment model, and training the data set subjected to normalization processing by using the judgment model to obtain a judgment model parameter;
and S5, after training is finished, judging by using the established judgment model according to the test data set, marking abnormal points, outputting results, and verifying the effect of auxiliary judgment of the judgment model.
The technical scheme of the invention is further specifically explained by specific calculation examples.
(1) Data samples
And sorting and extracting qualified transformer sampling detection data of 2016, 2017 and 2018 of a certain power saving network, namely all positive samples. The detection data comprise direct current resistance, short-circuit resistance, ground resistance, unbalance rate, no-load loss experimental data, short-circuit loss experimental data, induction voltage-withstanding and external voltage-withstanding experimental data, temperature rise experimental data and the like of the transformer, and the quality level of the transformer can be comprehensively reflected.
The obtained data are classified according to the transformer capacity, wherein the transformer data with the capacity of 10kV (100KVA) which accounts for the highest proportion of the data are collected and are arranged into a data characteristic sample set. The data generated by tests performed on a transformer is taken as a characteristic attribute of a sample. The data is cleaned and removed incompletely, and the finally obtained training set has 107 samples in total, and each sample contains 59 characteristic attributes.
And determining the abnormal point detection effect of each model, and manually constructing a test set in which the abnormal points are distributed for testing. The test set used herein contains 97 samples, each sample contains 59 attributes, wherein 5 points numbered 92 to 96 are artificially constructed abnormal samples, and the detection data of the abnormal samples are in accordance with the power grid transformer standard, but have a certain difference with the detection data of the normal transformer.
(2) Model training
① model implementation
Using the python development platform of the sklern platform, the Oneclass SVM model can be directly established by the following statements:
OneclassSVM(nu＝,kernel＝,gamma＝)
wherein nu represents the number of abnormal points in the training set, the value range is (0, 1), kernel represents the selected kernel function, various kernel functions such as 'rbf', 'poly' and 'sigmoid' can be selected, gamma is a parameter of the kernel functions such as 'rbf', 'poly' or 'sigmoid' and is used for controlling the continuity of the boundary between the normal points and the abnormal points, the lower the gamma, the higher the continuity of the boundary, the higher the continuity, the more the normal data points are surrounded by a hypersphere, otherwise, the more the hypersphere respectively surround the normal data points, but overfitting is easy to occur.
② OCSVM parameter tuning
For training data, all the transformers are defaulted to be positive samples according to the detection result of a certain provincial power grid, namely all the transformers are qualified products, and the parameter nu cannot be 0, so that the parameter nu is set to be 0.01 which is closer to the actual failure rate.
Setting kernel function parameters:
the commonly used kernel functions of the OCSVM model include polynomial kernel functions, RBF kernel functions, gaussian kernel functions, etc., and the optimal kernel functions and parameters are usually selected according to the trial calculation result. By comparative analysis, the mean of the gaussian kernel was chosen to be 0 and the variance was chosen to be 500.
Because the training sample data used for training are all qualified transformers which pass through the power grid spot check, when the proportion of abnormal points is set to be 0.01 for the output results of the machine learning algorithms, the closer the corresponding quantity of the theoretically detected abnormal points is to the proportion, the better the fitting degree of the model is; when the abnormal point ratio is set to 0, theoretically, the fewer abnormal points in the training data are detected, the better the fitting degree of the model is. From the program tuning operation result, the parameter configuration is nu equal to 0.01, kernel equal to 'rbf', and the fitting degree of gamma equal to 0.0011 is very good.
(3) Testing
The test samples described above were tested using an OCSVM model with parameters nu 0.01, kernel rbf, and gamma 0.0011, the set of test samples comprising 97 samples, each sample containing 59 attributes, of which 5 points numbered 92 to 96 are artificially constructed abnormal samples.
In the test results, the distance of each data point from the boundary hypersphere is shown in FIG. 2,
it can be seen that there are 5 negative distance samples, numbered 92, 93, 94, 95, 96, i.e. 5 outliers are detected.
Therefore, the suspected abnormality detection report can be effectively found out by using the OCSVM algorithm.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. An auxiliary evaluation method for detection results of a distribution transformer is characterized by comprising the following specific steps:
s1, sorting and extracting qualified sampling detection data of the distribution transformer, classifying the obtained data according to transformer capacity, sorting the transformer data with the capacity of 10kV, which accounts for the highest proportion of the data, into a data characteristic sample set, regarding the transformer as a sample, and regarding data generated by various tests performed on the transformer as the characteristic attribute of the sample;
s2, performing data cleaning on the data characteristic sample set, removing blank or repeated detection data, and converting the data characteristic sample set subjected to data cleaning into a vector data set;
s3, carrying out normalization processing on the data characteristic quantity by using a Z-score normalization method, wherein the processed data conform to standard normal distribution, namely the mean value is 0, the standard deviation is 1, and the conversion function is as follows:
in the formula, x*Is the value of Z, x is the normalized random variable, u is the sample mean, σ is the sample standard deviation;
s4, establishing a single-class support vector machine judgment model, and training the data set subjected to normalization processing by using the judgment model to obtain a judgment model parameter;
and S5, after training is finished, judging by using the established judgment model according to the test data set, marking abnormal points, outputting results, and verifying the effect of auxiliary judgment of the judgment model.
2. The auxiliary evaluation method for the distribution transformer detection result according to claim 1, wherein the detection data in the step S1 includes dc resistance, short-circuit resistance, ground resistance, unbalance rate, no-load loss experimental data, short-circuit loss experimental data, induced withstand voltage and applied withstand voltage experimental data, and temperature rise experimental data of the transformer, and can relatively comprehensively reflect the quality level of the transformer.
3. The auxiliary evaluation method for the distribution transformer detection result according to claim 1, wherein in step S4, a python development platform of a sklern platform is used, and the single-class support vector machine model is directly built by the following statements:
OCSVM(nu＝,kernel＝,gamma＝)
wherein nu represents the number of abnormal points in the training set, and the value range is [0, 1 ]; kernel represents the selected kernel function, and various kernel functions of 'rbf', 'poly' and 'sigmoid' are available; gamma is a parameter of the 'rbf', 'poly' or 'sigmoid' kernel function for controlling the continuity of the boundary between the normal point and the abnormal point, the lower the gamma, the higher the continuity of the boundary, the more likely the continuity is to surround all the normal data points with one hypersphere, and vice versa, the more likely the hypersphere is to surround the normal data points respectively, but overfitting is easy to occur.
4. The method of claim 1, wherein the test data set in step S5 is a artificially constructed test set with known distribution of outliers.
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Application publication date: 20200114 |