CN112232386B - Voltage sag severity prediction method based on support vector machine - Google Patents
Voltage sag severity prediction method based on support vector machine Download PDFInfo
- Publication number
- CN112232386B CN112232386B CN202011037249.1A CN202011037249A CN112232386B CN 112232386 B CN112232386 B CN 112232386B CN 202011037249 A CN202011037249 A CN 202011037249A CN 112232386 B CN112232386 B CN 112232386B
- Authority
- CN
- China
- Prior art keywords
- data
- voltage sag
- model
- training
- support vector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012706 support-vector machine Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012544 monitoring process Methods 0.000 claims abstract description 27
- 238000012549 training Methods 0.000 claims description 57
- 238000012360 testing method Methods 0.000 claims description 41
- 230000006870 function Effects 0.000 claims description 34
- 238000004364 calculation method Methods 0.000 claims description 10
- 239000013598 vector Substances 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 6
- 230000009977 dual effect Effects 0.000 claims description 6
- 238000012806 monitoring device Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000000691 measurement method Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000013058 risk prediction model Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
Abstract
The invention relates to a voltage sag severity prediction method based on a support vector machine, which establishes a voltage sag severity prediction model based on the support vector machine, predicts the severity of a voltage sag by analyzing voltage sag historical monitoring data by using the model, and realizes the function of predicting the voltage sag severity so as to reduce economic loss caused by the voltage sag.
Description
Technical Field
The invention relates to the field of power systems, in particular to a voltage sag severity prediction method based on a support vector machine.
Background
In order to realize safer, more reliable and more accurate production of products for enterprises and related equipment, in the current rapidly developed power system, higher requirements on the power quality are also provided. The problem of voltage sag affects the quality of electric energy to a great extent, the working state of equipment is changed due to the occurrence of the voltage sag, and finally the quality of products is affected, so that a user suffers huge economic loss, and particularly, the loss caused by the voltage sag is more serious in some high and new technology industries.
The current voltage sag severity prediction and evaluation mainly comprises an actual measurement method and a simulation method, wherein the actual measurement method adopts related equipment to monitor all indexes in an omnibearing manner to evaluate the severity, the equipment required by the method is complex and numerous, and meanwhile, the method has higher equipment requirements, higher cost and lower economical efficiency and is not suitable for enterprise users. The simulation method is used for setting faults based on statistics and probability principles to carry out prediction and evaluation on the severity of voltage sag, and is long in calculation time under a complex large power grid, complex in calculation, greatly influenced by fault information, low in accuracy of the obtained results and unsuitable for the complex and large-scale power system network at present.
Disclosure of Invention
Therefore, the invention aims to provide a voltage sag severity prediction method based on a support vector machine, which establishes a voltage sag severity prediction model based on the support vector machine, and predicts the severity of a voltage sag by analyzing voltage sag history monitoring data by using the model, so as to realize the prediction of the severity of the voltage sag.
The invention is realized by adopting the following scheme: a voltage sag severity prediction method based on a support vector machine comprises the following steps:
step S1: the voltage sag historical monitoring data recorded by the power quality monitoring device are measured and collected and are divided into two types of training data and test data, wherein the proportion of the training data to the test data is 8:2, training data are used for training and learning the support vector machine model, and test data are used for testing and checking the support vector machine model;
step S2: normalizing the voltage sag history monitoring data to be between 0 and 1 by adopting a deviation normalization method, and taking the normalized voltage sag history monitoring data as input data for predicting the severity of the voltage sag, and simultaneously taking the severity of the voltage sag of a node to be evaluated in a power grid as output data, and storing the input data and the output data;
step S3: selecting a Support Vector Machine (SVM) type: an RBF function is selected as a kernel function to construct a support vector machine model;
step S4: training the support vector machine model by using the normalized voltage sag history monitoring data, and learning by taking the normalized voltage sag history monitoring data as training sample input; judging whether the model learning is finished, if the termination criterion is smaller than the preset value of the tolerable deviation, finishing the model learning, if so, executing the step S5, otherwise, continuing the model learning;
step S5: inputting test data to test the model, and adjusting and optimizing punishment parameters C, insensitive loss parameters epsilon and gamma values of the model according to the error magnitude and the change trend of the output results of the training data and the test data to obtain a voltage sag severity prediction model based on a support vector machine;
step S6: after the parameter adjustment is optimized, the data is input into the voltage sag severity prediction model based on the support vector machine obtained in the step S5 by using the currently known data, and the data is processed and analyzed by using the voltage sag severity prediction model based on the support vector machine by using the voltage sag severity prediction function of the model after training, so that the voltage sag severity of a certain node is obtained, and a user can know the voltage sag according to the obtained result.
Further, the voltage sag history monitoring data comprises residual voltage amplitude, duration, voltage class, monitoring position and operation mode of the voltage sag.
Further, the specific content of the step S3 is as follows:
sample set for support vector machine { (X) 1 ,y 1 ),(X 2 ,y 2 )…(X n ,y n )}，X i＝1,2…n For input vector, y i For output vector, its corresponding relation is:
in the method, in the process of the invention,representing the corresponding vector after mapping X to the high-dimensional space;
the dual problem is obtained according to Lagrange dual theory:
kernel functionSubstituting and solving to obtain:
the RBF function has small influence on the complexity of the model in the calculation process and nonlinear characteristics and fitting capacity, so that the RBF function is used as a kernel function of the support vector machine;
sigma represents a parameter that can be adjusted;
after the data normalization and the kernel function establishment are completed, step S4 may be executed.
Further, in step S5, the specific contents of the adjustment and optimization of the penalty parameter C, the insensitive loss parameter epsilon, and the gamma value of the model are:
at the addition of relaxation variable ζ i，i＝1…n Then, the model introduces fault tolerance to the data, and the objective function and constraint conditions between the support vector machines become:
c is a punishment coefficient, the degree of fitting of the C value to the training data is high, overfitting is easy to occur, generalization capability is poor, under fitting of the C value to the data with small C value occurs, and the degree of fitting to the training data is not high, so that the C value needs to be adjusted according to the output result and the error of the training data and the test data, when the error of the result obtained by the training data is small, the error of the result obtained by the test data is large, the difference between the accuracy of the training result and the accuracy of the test result is larger than 15%, the C value is considered to be adjusted, and when the error of the result obtained by the training data is larger than an error allowable value, the C value is increased, and finally the reasonable C value is obtained; determining a reasonable epsilon value through the repeated calculation result of the training data and the test data, wherein the epsilon value is selected differently according to the different data quantity; the gamma value is a parameter carried by the RBF function as a kernel function, and has the following relation:
thus (2)
After the training data, the test data result and the error analysis are repeatedly performed, the gamma value is adjusted according to the error magnitude and the change trend, when the accuracy of the training result is high and the accuracy of the test data result is low, the difference of the accuracy of the training result and the accuracy of the test data result is more than 15%, the gamma value needs to be considered to be adjusted, and when the error of the training result is more than the error allowable range, the gamma value is increased, so that the accuracy and the efficiency of the model are improved.
Compared with the prior art, the invention has the following beneficial effects:
the invention can predict and evaluate the severity of a certain voltage sag. The method is characterized in that data in the existing power quality monitoring device of the power system are utilized, a voltage sag severity prediction model based on a support vector machine is constructed on the basis of the support vector machine, and the function of predicting the voltage sag severity is achieved. Meanwhile, the invention utilizes the existing electric energy quality monitoring device in the electric power system, does not need to adopt a great deal of equipment in an actual measurement method, improves the economy, simultaneously avoids complex calculation of a simulation method, and can accurately, economically and rapidly predict and evaluate the severity of the voltage sag.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
As shown in fig. 1, the present embodiment provides a voltage sag severity prediction method based on a support vector machine, which includes the following steps:
step S1: the voltage sag historical monitoring data recorded by the power quality monitoring device are measured and collected and are divided into two types of training data and test data, wherein the proportion of the training data to the test data is 8:2, when the data quantity is large, the proportion can be properly adjusted, training data are used for training and learning the support vector machine model, and test data are used for testing and checking the support vector machine model;
step S2: normalizing the voltage sag history monitoring data to be between 0 and 1 by adopting a deviation normalization method, and taking the normalized voltage sag history monitoring data as input data for predicting the severity of the voltage sag, and simultaneously taking the severity of the voltage sag of a node to be evaluated in a power grid as output data, and storing the input data and the output data;
step S3: selecting a Support Vector Machine (SVM) type: because the RBF function has nonlinearity, the complexity of the model is not changed in the process of calculation due to the adjustment of parameters, and in the past research, the RBF-based SVM algorithm has excellent fitting capability, so that the RBF function is selected as a kernel function to construct a support vector machine model;
step S4: training the support vector machine model by using the normalized voltage sag history monitoring data, and learning by taking the normalized voltage sag history monitoring data as training sample input; judging whether the model learning is finished, if the termination criterion is smaller than the preset value of the tolerable deviation, finishing the model learning, if so, executing the step S5, otherwise, continuing the model learning; in this embodiment, the preset value of the tolerable deviation is 0.001;
step S5: inputting test data to test the model, and adjusting and optimizing punishment parameters C, insensitive loss parameters epsilon and gamma values of the model according to the error magnitude and the change trend of the output results of the training data and the test data to obtain a voltage sag severity prediction model based on a support vector machine;
step S6: after the parameter adjustment is optimized, the data is input into the voltage sag severity prediction model based on the support vector machine obtained in the step S5 by using the currently known data, and the data is processed and analyzed by using the voltage sag severity prediction model based on the support vector machine by using the voltage sag severity prediction function of the model after training, so that the voltage sag severity of a certain node is obtained, and a user can know the voltage sag according to the obtained result.
In this embodiment, the voltage sag history monitoring data includes a residual voltage amplitude, a duration, a voltage level, a monitoring position, and an operation mode of the voltage sag.
In this embodiment, the specific content of step S3 is as follows:
sample set for support vector machine { (X) 1 ,y 1 ),(X 2 ,y 2 )…(X n ,y n )}，X i＝1,2…n For input vector, y i For output vector, its corresponding relation is:
in the method, in the process of the invention,representing the corresponding vector after mapping X to the high-dimensional space;
the dual problem is obtained according to Lagrange dual theory:
kernel functionSubstituting and solving to obtain:
the RBF function has small influence on the complexity of the model in the calculation process and nonlinear characteristics and fitting capacity, so that the RBF function is used as a kernel function of the support vector machine;
sigma represents a parameter that can be adjusted;
after the data normalization and the kernel function establishment are completed, step S4 may be executed.
In this embodiment, the specific contents of the adjustment and optimization of the penalty parameter C, the insensitive loss parameter epsilon and the gamma value of the model in step S5 are:
at the addition of relaxation variable ζ i，i＝1…n Then, the model introduces fault tolerance to the data, and the objective function and constraint conditions between the support vector machines become:
c is a punishment coefficient, the fitting data of the training data is high due to the fact that the C value is large, the fitting is easy to occur, the generalization capability is poor, the fitting is insufficient due to the fact that the C value is too small, the fitness of the training data is not high, therefore, the C value needs to be adjusted according to the output result and the error of the training data and the test data, when the error of the result obtained by the training data is small, the error of the result obtained by the test data is large, the difference between the accuracy of the training result and the accuracy of the test result is larger than 15%, the C value is considered to be adjusted, and when the error of the result obtained by the training data is larger than the error allowable value, the C value is increased, and finally the reasonable C value is obtained; the introduction of the insensitive loss parameter epsilon can neglect the deviation of the true value within a certain degree, a reasonable epsilon value is determined through the repeated calculation result of the training data and the test data, and the epsilon value is selected differently according to the different data quantity; the gamma value is a parameter carried by the RBF function as a kernel function, and has the following relation:
thus (2)
The gamma value affects the distribution condition of the data mapped to a new feature space, the gamma value is overlarge, the sigma value is small, the Gaussian radial basis function is narrow, the data prediction accuracy is high, but the adaptability to unknown samples is not high, the data is fitted excessively, the gamma value is too small, the obtained results are opposite, and meanwhile, the gamma value has a certain influence on the number of support vectors so as to affect the speed of data training and prediction, therefore, after the training data, the test data result and the error analysis thereof are repeatedly analyzed, the gamma value is adjusted according to the error size and the change trend, when the training result accuracy is high, the test data result accuracy is small, the difference of the accuracy of the two is more than 15%, the gamma value needs to be considered to be adjusted, and when the training result error is more than the error allowable range, the gamma value is increased so as to improve the accuracy and the efficiency of the model.
Preferably, in this embodiment, the support vector machine is used as a model basis to analyze and process the data, thereby exerting its advantages and predicting the severity of the voltage sag. Support vector machines (Support Vector Machine, SVM) are an important way of machine learning. Even under the condition of a small number of samples, the SVM can neutralize the complexity of the model and has a certain new sample adaptability so as to obtain better popularization capability, and the SVM is suitable for solving the problem of data fitting prediction.
Preferably, the present embodiment:
(1) Predicting the severity of a voltage sag by using the voltage sag history monitoring data;
(2) Constructing a foundation by taking a support vector machine as a model, and realizing a voltage sag severity prediction function;
(3) The voltage sag historical monitoring data is analyzed and processed through a support vector machine, the severity degree of a voltage sag is predicted to be obtained, and a basis is provided for preventing and controlling the voltage sag.
Preferably, in the present embodiment, voltage sag history monitoring data of the power quality monitoring device is collected, and the data are used as data support for predicting the severity of the voltage sag and stored in a centralized manner as input data for predicting the severity of the voltage sag. And then, establishing a voltage sag risk prediction model based on the support vector machine, wherein the model is based on the support vector machine, the model is learned through the voltage sag history monitoring data, the model is continuously optimized and adjusted in the learning process, and after the learning is completed, the voltage sag severity prediction model based on the support vector machine is obtained, namely, the voltage sag severity prediction can be carried out through inputting related data.
The foregoing description is only of the preferred embodiments of the invention, and all changes and modifications that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (1)
1. A voltage sag severity prediction method based on a support vector machine is characterized by comprising the following steps of: the method comprises the following steps:
step S1: the voltage sag historical monitoring data recorded by the power quality monitoring device are measured and collected and are divided into two types of training data and test data, wherein the proportion of the training data to the test data is 8:2, training data are used for training and learning the support vector machine model, and test data are used for testing and checking the support vector machine model;
step S2: normalizing the voltage sag history monitoring data to be between 0 and 1 by adopting a deviation normalization method, and taking the normalized voltage sag history monitoring data as input data for predicting the severity of the voltage sag, and simultaneously taking the severity of the voltage sag of a node to be evaluated in a power grid as output data, and storing the input data and the output data;
step S3: selecting a Support Vector Machine (SVM) type: an RBF function is selected as a kernel function to construct a support vector machine model;
step S4: training the support vector machine model by using the normalized voltage sag history monitoring data, and learning by taking the normalized voltage sag history monitoring data as training sample input; judging whether the model learning is finished, if the termination criterion is smaller than the preset value of the tolerable deviation, finishing the model learning, if so, executing the step S5, otherwise, continuing the model learning;
step S5: inputting test data to test the model, and adjusting and optimizing punishment parameters C, insensitive loss parameters epsilon and gamma values of the model according to the error magnitude and the change trend of the output results of the training data and the test data to obtain a voltage sag severity prediction model based on a support vector machine;
step S6: after the parameter adjustment is optimized, the data is input into a voltage sag severity prediction model based on a support vector machine, which is obtained in the step S5, by utilizing the voltage sag severity prediction function of the model after training, the data is processed and analyzed based on the voltage sag severity prediction model of the support vector machine, so that the voltage sag severity of a certain node is obtained, and a user can know the voltage sag according to the obtained result;
the voltage sag history monitoring data comprise residual voltage amplitude, duration, voltage class, monitoring position and operation mode of the voltage sag;
the specific content of the step S3 is as follows:
sample set for support vector machine { (X) 1 ,y 1 ),(X 2 ,y 2 )…(X n ,y n )}，X i＝1,2…n For input vector, y i For output vector, its corresponding relation is:
in the method, in the process of the invention,representing the corresponding vector after mapping X to the high-dimensional space;
the dual problem is obtained according to Lagrange dual theory:
kernel functionSubstituting and solving to obtain:
the RBF function has small influence on the complexity of the model in the calculation process and nonlinear characteristics and fitting capacity, so that the RBF function is used as a kernel function of the support vector machine;
sigma representsParameters that can be adjusted; after the data normalization and the establishment of the kernel function are completed, the step S4 can be executed;
in step S5, the specific contents of adjusting and optimizing the penalty parameter C, the insensitive loss parameter epsilon and the gamma value of the model are as follows:
at the addition of relaxation variable ζ i，i＝1…n Then, the model introduces fault tolerance to the data, and the objective function and constraint conditions between the support vector machines become:
c is a punishment coefficient, the degree of fitting of the C value to the training data is high, overfitting is easy to occur, generalization capability is poor, under fitting of the C value to the data with small C value occurs, and the degree of fitting to the training data is not high, so that the C value needs to be adjusted according to the output result and the error of the training data and the test data, when the error of the result obtained by the training data is small, the error of the result obtained by the test data is large, the difference between the accuracy of the training result and the accuracy of the test result is larger than 15%, the C value is considered to be adjusted, and when the error of the result obtained by the training data is larger than an error allowable value, the C value is increased, and finally the reasonable C value is obtained; determining a reasonable epsilon value through the repeated calculation result of the training data and the test data, wherein the epsilon value is selected differently according to the different data quantity; the gamma value is a parameter carried by the RBF function as a kernel function, and has the following relation:
thus (2)
After the training data, the test data result and the error analysis are repeatedly performed, the gamma value is adjusted according to the error magnitude and the change trend, when the accuracy of the training result is high and the accuracy of the test data result is low, the difference of the accuracy of the training result and the accuracy of the test data result is more than 15%, the gamma value needs to be considered to be adjusted, and when the error of the training result is more than the error allowable range, the gamma value is increased, so that the accuracy and the efficiency of the model are improved.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011037249.1A CN112232386B (en) | 2020-09-27 | 2020-09-27 | Voltage sag severity prediction method based on support vector machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011037249.1A CN112232386B (en) | 2020-09-27 | 2020-09-27 | Voltage sag severity prediction method based on support vector machine |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112232386A CN112232386A (en) | 2021-01-15 |
| CN112232386B true CN112232386B (en) | 2024-03-15 |
Family
ID=74120270
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011037249.1A Active CN112232386B (en) | 2020-09-27 | 2020-09-27 | Voltage sag severity prediction method based on support vector machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112232386B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115696361A (en) * | 2021-07-13 | 2023-02-03 | 中兴通讯股份有限公司 | Parameter setting method and device, electronic equipment and storage medium |
| CN113919162B (en) * | 2021-10-15 | 2022-08-09 | 福州大学 | Voltage sag risk early warning method based on simulation and multi-source measured data fusion |
| CN115469154B (en) * | 2022-11-02 | 2023-03-31 | 国网信息通信产业集团有限公司 | Voltage sag duration type prediction method and system and prediction terminal |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105548862A (en) * | 2016-01-25 | 2016-05-04 | 合肥工业大学 | Simulation circuit fault diagnosis method on the basis of generalized multi-nuclear support vector machine |
| CN107123987A (en) * | 2017-05-11 | 2017-09-01 | 华东交通大学 | Electrical energy power quality disturbance recognition methods based on on-line training weighed SVM |
| CN108564204A (en) * | 2018-03-23 | 2018-09-21 | 西安理工大学 | Least square method supporting vector machine power predicating method based on maximal correlation entropy criterion |
| CN109470985A (en) * | 2018-06-19 | 2019-03-15 | 国网浙江省电力有限公司湖州供电公司 | A kind of voltage sag source identification methods based on more resolution singular value decompositions |
| WO2019202494A1 (en) * | 2018-04-16 | 2019-10-24 | Ashish Sharma | A system and a method for predicting failure risk of appliances |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11486925B2 (en) * | 2020-05-09 | 2022-11-01 | Hefei University Of Technology | Method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation |
-
2020
- 2020-09-27 CN CN202011037249.1A patent/CN112232386B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105548862A (en) * | 2016-01-25 | 2016-05-04 | 合肥工业大学 | Simulation circuit fault diagnosis method on the basis of generalized multi-nuclear support vector machine |
| CN107123987A (en) * | 2017-05-11 | 2017-09-01 | 华东交通大学 | Electrical energy power quality disturbance recognition methods based on on-line training weighed SVM |
| CN108564204A (en) * | 2018-03-23 | 2018-09-21 | 西安理工大学 | Least square method supporting vector machine power predicating method based on maximal correlation entropy criterion |
| WO2019202494A1 (en) * | 2018-04-16 | 2019-10-24 | Ashish Sharma | A system and a method for predicting failure risk of appliances |
| CN109470985A (en) * | 2018-06-19 | 2019-03-15 | 国网浙江省电力有限公司湖州供电公司 | A kind of voltage sag source identification methods based on more resolution singular value decompositions |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112232386A (en) | 2021-01-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112232386B (en) | Voltage sag severity prediction method based on support vector machine | |
| CN113656938A (en) | Monitoring method, control device and storage medium for distribution network transformer | |
| CN110874685A (en) | Intelligent electric energy meter running state distinguishing method and system based on neural network | |
| CN112255484A (en) | Lightning arrester operation state online monitoring and assessment method and system | |
| CN112307963A (en) | Converter transformer running state identification method based on vibration signals | |
| CN108318810A (en) | High voltage isolator fault determination method and device | |
| CN110865924A (en) | Health degree diagnosis method and health diagnosis framework for internal server of power information system | |
| CN110443481B (en) | Power distribution automation terminal state evaluation system and method based on hybrid K-nearest neighbor algorithm | |
| CN104866679A (en) | Mechanical characteristic prediction method of switching device | |
| CN115408864A (en) | Electronic transformer error state self-adaptive prediction method, system and equipment | |
| CN105471624A (en) | Comprehensive evaluation method for access of communication equipment to terminal communication access network | |
| CN115169704A (en) | CVT error state prediction method and device based on increment integrated learning model | |
| CN111507565A (en) | Performance evaluation method and system of energy storage power station in frequency modulation application scene | |
| CN111680712A (en) | Transformer oil temperature prediction method, device and system based on similar moments in the day | |
| CN112926620B (en) | Defect type identification method and system for partial discharge of transformer bushing | |
| CN117154263A (en) | Lithium battery cascade utilization charging and discharging system and control method | |
| CN116662925A (en) | Industrial process soft measurement method based on weighted sparse neural network | |
| CN115267586A (en) | Lithium battery SOH evaluation method | |
| CN112307652B (en) | Quick maintenance method for complex equipment system based on average remaining life importance | |
| CN106127326B (en) | Chemical material processing melt index forecasting method | |
| CN117909855B (en) | Data monitoring and auxiliary governance method based on electric power model | |
| CN116953529B (en) | Lithium ion power battery health assessment method and system based on data fusion | |
| Basia et al. | Comparison of data driven algorithms for SoH estimation of Lithium-ion batteries | |
| Ajane et al. | State of Charge Estimation of Li-Ion Batteries Using Random Forest Regression Model with Modified Parameters for Multiple Cycles | |
| Sun et al. | Peak Power Estimation Considering Initial Polarization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |