CN117909652A - High-voltage circuit breaker fault diagnosis data processing method - Google Patents
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
- G01R31/3271—Testing of circuit interrupters, switches or circuit-breakers of high voltage or medium voltage devices
- G01R31/3272—Apparatus, systems or circuits therefor
- G01R31/3274—Details related to measuring, e.g. sensing, displaying or computing; Measuring of variables related to the contact pieces, e.g. wear, position or resistance
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- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
- G01R31/3271—Testing of circuit interrupters, switches or circuit-breakers of high voltage or medium voltage devices
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
The invention relates to the field of fault diagnosis of high-voltage circuit breakers, in particular to a fault diagnosis data processing method of a high-voltage circuit breaker, which comprises the following steps: s1, acquiring signal parameters of a high-voltage circuit breaker under various working conditions through a data acquisition module, and carrying out morphological denoising through a filter; s2, extracting a feature vector according to the denoised signal parameters obtained in the step S1 to be used as sample data; s3, balancing the sample number under different working conditions in the step S2; and S4, training a fault diagnosis model through the machine learning algorithm according to the sample data balanced in the step S3. The high-voltage circuit breaker fault diagnosis data processing method provided by the invention can be used for denoising the initial signal and balancing various sample data, so that the effect of improving the fault diagnosis precision is achieved.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of high-voltage circuit breakers, in particular to a fault diagnosis data processing method of a high-voltage circuit breaker.
Background
High voltage circuit breakers are critical components of an electrical power system, which function in the electrical power system to control (switch loads) and protect (cut off faults), the operating state of which is directly related to the safety and stability of the line in which they are located. According to the actual operation requirement of the power system, the high-voltage circuit breaker plays a control role in the power system to implement the investment and cutting operation on a specific line, and on the other hand, the high-voltage circuit breaker plays a protection role in the power system, and when a short-circuit fault occurs in the system, the high-voltage circuit breaker rapidly cuts off huge short-circuit current to protect the whole power system from further damage. Once the high-voltage circuit breaker fails, the safety and stability of the whole power system are directly jeopardized.
The switching on and off of the high-voltage circuit breaker is mainly completed by a mechanical structure, and the mechanical structure is required to transmit high power and heavy load in order to realize quick action. With the increase of the number of actions, the mechanical performance of the high-voltage circuit breaker inevitably deteriorates. Some domestic and foreign investigation and research on the reliability of the high-voltage circuit breaker show that mechanical faults are main factors of failure of the high-voltage circuit breaker, and most of the mechanical faults occur in an operating mechanism.
The signals commonly used in the state monitoring and fault diagnosis research of the high-voltage circuit breaker are not very much coil current signals, moving contact stroke curve signals, vibration signals and sound signals, however, due to various factors, the acquired signals often have interference noise; on the other hand, since the high-voltage circuit breaker is in a stable working state most of the time, the normal sample data volume is far larger than the fault sample data, so that the sample data of different types are seriously unbalanced, and the training of a fault diagnosis model is influenced.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present invention has been made in view of the above-mentioned problems with the conventional high voltage circuit breaker failure diagnosis data processing method.
In order to solve the technical problems, the invention provides the following technical scheme: a fault diagnosis data processing method of a high-voltage circuit breaker comprises the following steps:
S1, acquiring signal parameters of a high-voltage circuit breaker under various working conditions through a data acquisition module, carrying out morphological denoising through a filter, detecting a vibration curve of a moving contact through a vibration sensor, carrying out function compounding on a record of the highest point value of the vibration curve, carrying out function compounding on a record of the lowest point parameter, carrying out function compounding on the midpoint of the record of the lowest point value, and the like to obtain three function curves, recording intersection point position parameters of the three function curves, and obtaining voltage parameters, electric power parameters and formation parameters similar to a simplified function through function conversion by adopting the method to obtain an inspection function;
s2, extracting an intersection point position parameter according to the intersection point function obtained in the step S1, determining a feature vector through the intersection point, and using the extracted feature vector as sample data;
s3, balancing the sample number under different working conditions in the step S2;
and S4, training a fault diagnosis model through the machine learning algorithm according to the checking function in the step S1 and the sample data after balancing in the step S3.
As a preferred embodiment of the high-voltage circuit breaker fault diagnosis data processing method of the present invention, wherein: in the step S1, the signal parameters include a vibration signal, a current signal and a moving contact travel curve signal, the data acquisition module includes an acceleration sensor, an angle sensor and a hall current sensor, the acceleration sensor is fixedly installed near a closing electromagnet, near a separating electromagnet or at one place of a beam of an operating mechanism of the high-voltage circuit breaker, the angle sensor is fixedly installed on a separating and closing indicator pin of the high-voltage circuit breaker, and the hall current sensor is fixedly installed near a closing coil of the high-voltage circuit breaker.
As a preferred embodiment of the high-voltage circuit breaker fault diagnosis data processing method of the present invention, wherein: in the step S1, the multiple working conditions include a normal running state and multiple fault states, and the multiple fault states at least include a base bolt loosening fault state, a buffer spring fatigue fault state, a transmission mechanism fault state and a closing spring fatigue fault state.
As a preferred embodiment of the high-voltage circuit breaker fault diagnosis data processing method of the present invention, wherein: in the step S2, the feature vector is extracted in the following manner: and dividing the vibration signal of the high-voltage circuit breaker into three sections by taking the closing start time and closing end time of the high-voltage circuit breaker as demarcation points, respectively calculating the signal energy entropy corresponding to each time section, and combining the energy entropy of each section of signal as a characteristic vector.
As a preferred embodiment of the high-voltage circuit breaker fault diagnosis data processing method of the present invention, wherein: in the step S3, the method for balancing the sample number under different working conditions is undersampling, and the specific algorithm steps are as follows:
S1: scanning an original data set O, assuming that the set Omaj is a majority class sample set, and the label is-1, letting omin=o-Omaj, wherein Omin is a minority class sample set, the label is 1, the training set t=omin, and letting U (sample, δ) represent a δ neighborhood of the sample;
S2: if there are few classes of samples in U (sample, delta), then the sample is marked as delta-unviable; if there are no few classes of samples in U (sample, delta), then the sample is marked as delta-reachable;
S3: and then, calling a Bayesian classification algorithm on the training set T to obtain a classifier h δ: x → { -1,1}; calculating an AUG δ value of the classifier h δ by using the test set;
S4: calculating a neighborhood radius delta *,δ* =argmax { augdelta } corresponding to the classifier with the maximum AUG δ value;
S5, determining the real boundary of the Bayesian classifier of the undersampling method based on the neighborhood radius delta * corresponding to the classifier.
As a preferred embodiment of the high-voltage circuit breaker fault diagnosis data processing method of the present invention, wherein: the specific method for balancing the sample number under different working conditions through the oversampling comprises the following steps: and respectively carrying out two-class oversampling on the samples of each minority class and the samples of the maximum number of classes, and synthesizing new samples of the minority classes until the number of the samples of each class is the same.
As a preferred embodiment of the high-voltage circuit breaker fault diagnosis data processing method of the present invention, wherein: the specific method for balancing the sample number under different working conditions through undersampling comprises the following steps: and respectively carrying out two-class undersampling on the samples of each minority class and the samples of the maximum number class, and deleting part of the samples of the maximum number class until the number of the samples of each class is the same.
As a preferred embodiment of the high-voltage circuit breaker fault diagnosis data processing method of the present invention, wherein: the machine learning algorithm at least comprises one of a random forest, a K nearest neighbor method, a support vector machine, a kernel extreme learning machine or a generalized regression neural network.
The invention has the beneficial effects that: according to the fault diagnosis data processing method of the high-voltage circuit breaker, the initial signal is denoised, and various sample data are balanced in an undersampling processing mode, so that the effect of improving the fault diagnosis precision is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
Fig. 1 is a schematic flow chart of a fault diagnosis data processing method for a high-voltage circuit breaker.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1, for one embodiment of the present invention, there is provided a high voltage circuit breaker fault diagnosis data processing method including the steps of:
S1, acquiring signal parameters of a high-voltage circuit breaker under various working conditions through a data acquisition module, carrying out morphological denoising through a filter, detecting a vibration curve of a moving contact through a vibration sensor, carrying out function compounding on a record of the highest point value of the vibration curve, carrying out function compounding on a record of the lowest point parameter, carrying out function compounding on the midpoint of the record of the lowest point value, and the like to obtain three function curves, recording intersection point position parameters of the three function curves, and obtaining voltage parameters, electric power parameters and formation parameters similar to a simplified function through function conversion by adopting the method to obtain an inspection function;
Specifically, the signal parameters comprise vibration signals, current signals and moving contact travel curve signals, the data acquisition module comprises an acceleration sensor, an angle sensor and a Hall current sensor, the acceleration sensor is fixedly arranged near a closing electromagnet of the high-voltage circuit breaker, near a separating electromagnet or at one position of an operating mechanism beam, the angle sensor is fixedly arranged on a separating and closing indicating needle of the high-voltage circuit breaker, and the Hall current sensor is fixedly arranged near a closing coil of the high-voltage circuit breaker. The multiple working conditions comprise a normal running state and multiple fault states, and the multiple fault states at least comprise a base bolt loosening fault state, a buffer spring fatigue fault state, a transmission mechanism fault state and a closing spring fatigue fault state.
S2, extracting an intersection point position parameter according to the intersection point function obtained in the step S1, determining a feature vector through the intersection point, and using the extracted feature vector as sample data;
Specifically, the feature vector is extracted by: and dividing the vibration signal of the high-voltage circuit breaker into three sections by taking the closing start time and closing end time of the high-voltage circuit breaker as demarcation points, respectively calculating the signal energy entropy corresponding to each time section, and combining the energy entropy of each section of signal as a characteristic vector.
S3, balancing the sample number under different working conditions in the step S2;
specifically, the sample number under different working conditions is balanced by undersampling, and the specific algorithm comprises the following steps:
(1): scanning an original data set O, assuming that the set Omaj is a majority class sample set, and the label is-1, letting omin=o-Omaj, wherein Omin is a minority class sample set, the label is 1, the training set t=omin, and letting U (sample, δ) represent a δ neighborhood of the sample;
(2): if there are few classes of samples in U (sample, delta), then the sample is marked as delta-unviable; if there are no few classes of samples in U (sample, delta), then the sample is marked as delta-reachable;
(3): and then, calling a Bayesian classification algorithm on the training set T to obtain a classifier h δ: x → { -1,1};
calculating an AUG δ value of the classifier h δ by using the test set;
(4): calculating a neighborhood radius delta *,δ* =argmax { augdelta } corresponding to the classifier with the maximum AUG δ value;
(5) Based on the neighborhood radius delta * corresponding to the classifier, determining the real boundary of the Bayesian classifier of the undersampling method.
In addition, the sample number balancing method under different working conditions can be oversampling processing, namely, two-class oversampling is implemented on each minority class of samples and the maximum number of classes of samples respectively, and new minority class samples are synthesized until the sample number of each class is the same.
And S4, training a fault diagnosis model through the machine learning algorithm according to the checking function in the step S1 and the sample data after balancing in the step S3.
The machine learning algorithm at least comprises one of a random forest, a K nearest neighbor method, a support vector machine, a kernel extreme learning machine or a generalized regression neural network.
In summary, the fault diagnosis data processing method of the high-voltage circuit breaker disclosed by the invention balances various sample data through denoising an initial signal and through an oversampling or undersampling processing mode, thereby achieving the effect of improving the fault diagnosis precision.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (8)
1. A high voltage circuit breaker failure diagnosis data processing method, characterized by comprising:
S1, acquiring signal parameters of a high-voltage circuit breaker under various working conditions through a data acquisition module, carrying out morphological denoising through a filter, detecting a vibration curve of a moving contact through a vibration sensor, carrying out function compounding on a record of the highest point value of the vibration curve, carrying out function compounding on a record of the lowest point parameter, carrying out function compounding on the midpoint of the record of the lowest point value, and the like to obtain three function curves, recording intersection point position parameters of the three function curves, and obtaining voltage parameters, electric power parameters and formation parameters similar to a simplified function through function conversion by adopting the method to obtain an inspection function;
s2, extracting an intersection point position parameter according to the intersection point function obtained in the step S1, determining a feature vector through the intersection point, and using the extracted feature vector as sample data;
s3, balancing the sample number under different working conditions in the step S2;
and S4, training a fault diagnosis model through the machine learning algorithm according to the checking function in the step S1 and the sample data after balancing in the step S3.
2. A high voltage circuit breaker failure diagnosis data processing method according to claim 1, characterized in that: in the step S1, the signal parameters include a vibration signal, a current signal and a moving contact travel curve signal, the data acquisition module includes an acceleration sensor, an angle sensor and a hall current sensor, the acceleration sensor is fixedly installed near a closing electromagnet, near a separating electromagnet or at one place of a beam of an operating mechanism of the high-voltage circuit breaker, the angle sensor is fixedly installed on a separating and closing indicator pin of the high-voltage circuit breaker, and the hall current sensor is fixedly installed near a closing coil of the high-voltage circuit breaker.
3. A high voltage circuit breaker failure diagnosis data processing method according to claim 2, characterized in that: in the step S1, the multiple working conditions include a normal running state and multiple fault states, and the multiple fault states at least include a base bolt loosening fault state, a buffer spring fatigue fault state, a transmission mechanism fault state and a closing spring fatigue fault state.
4. A high voltage circuit breaker failure diagnosis data processing method according to claim 3, characterized in that: in the step S2, the feature vector is extracted in the following manner: and dividing the vibration signal of the high-voltage circuit breaker into three sections by taking the closing start time and closing end time of the high-voltage circuit breaker as demarcation points, respectively calculating the signal energy entropy corresponding to each time section, and combining the energy entropy of each section of signal as a characteristic vector.
5. The high voltage circuit breaker failure diagnosis data processing method according to claim 4, wherein: in the step S3, the method for balancing the sample numbers under different working conditions is undersampling or oversampling.
6. A high voltage circuit breaker failure diagnosis data processing method according to claim 5, characterized in that: the specific method for balancing the sample number under different working conditions through the oversampling comprises the following steps: and respectively carrying out two-class oversampling on the samples of each minority class and the samples of the maximum number of classes, and synthesizing new samples of the minority classes until the number of the samples of each class is the same.
7. The high voltage circuit breaker failure diagnosis data processing method according to claim 6, wherein: the specific algorithm steps for balancing the sample number under different working conditions through the undersampling are as follows:
S1: scanning an original data set O, assuming that the set Omaj is a majority class sample set, and the label is-1, letting omin=o-Omaj, wherein Omin is a minority class sample set, the label is 1, the training set t=omin, and letting U (sample, δ) represent a δ neighborhood of the sample;
S2: if there are few classes of samples in U (sample, delta), then the sample is marked as delta-unviable; if there are no few classes of samples in U (sample, delta), then the sample is marked as delta-reachable;
S3: and then, calling a Bayesian classification algorithm on the training set T to obtain a classifier h δ: x → { -1,1}; calculating an AUG δ value of the classifier h δ by using the test set;
S4: calculating a neighborhood radius delta *,δ* =argmax { augdelta } corresponding to the classifier with the maximum AUG δ value;
S5, determining the real boundary of the Bayesian classifier of the undersampling method based on the neighborhood radius delta * corresponding to the classifier.
8. The high voltage circuit breaker failure diagnosis data processing method according to claim 7, wherein: the machine learning algorithm at least comprises one of a random forest, a K nearest neighbor method, a support vector machine, a kernel extreme learning machine or a generalized regression neural network.
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| CN109188258A (en) * | 2018-07-17 | 2019-01-11 | 国网浙江省电力有限公司检修分公司 | The high-voltage circuitbreaker feature extraction and classification method being electrically coupled based on vibration |
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| WO2023044979A1 (en) * | 2021-09-27 | 2023-03-30 | 苏州大学 | Mechanical fault intelligent diagnosis method under class unbalanced dataset |
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| CN117272116A (en) * | 2023-10-13 | 2023-12-22 | 西安工程大学 | Transformer fault diagnosis method based on LORAS balance data set |
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| CN109188258A (en) * | 2018-07-17 | 2019-01-11 | 国网浙江省电力有限公司检修分公司 | The high-voltage circuitbreaker feature extraction and classification method being electrically coupled based on vibration |
| WO2023044979A1 (en) * | 2021-09-27 | 2023-03-30 | 苏州大学 | Mechanical fault intelligent diagnosis method under class unbalanced dataset |
| CN115468946A (en) * | 2022-10-12 | 2022-12-13 | 广东电网有限责任公司 | Transformer oil aging diagnosis method and device and storage medium |
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