CN115902476A - A multi-dimensional transformer fault diagnosis and state assessment system and method - Google Patents

Abstract

The invention provides a multidimensional transformer fault diagnosis and state evaluation system and method, which belong to the technical field of transformer fault diagnosis, and comprise a receiving module, a classification module, a diagnosis module, an analysis module and an evaluation module, wherein the receiving module is used for receiving historical operating parameters of a transformer uploaded by a user and analyzing the frequency of fault characteristic parameters; the classification module is used for receiving the current operation parameters of the transformer and extracting suspected class characteristic parameters; the diagnosis module is used for comparing the suspected category characteristic parameters with the frequency of the fault characteristic parameters to determine fault category characteristic parameters; the analysis module is used for determining the fault grade of the fault category characteristic parameter; the invention comprehensively judges the health state of the transformer, thereby comprehensively evaluating the fault of the transformer and making maintenance personnel make corresponding maintenance measures.

Description

A multi-dimensional transformer fault diagnosis and state assessment system and method

technical field

The invention belongs to the technical field of transformer fault diagnosis, and in particular relates to a multi-dimensional transformer fault diagnosis and state evaluation system and method.

Background technique

As one of the key pivotal equipment in the power system, the transformer runs continuously in the power system, and its own operating state directly affects the safe and stable operation of the entire power system. When the transformer has serious operating conditions and effective measures are not taken in time, it may cause fire, explosion, and even large-scale power outages. Therefore, when a transformer fails, it is of great significance to accurately determine the type of transformer fault through fault diagnosis technology, which can facilitate the staff to take corresponding maintenance measures in time, reduce the loss of faults, and avoid the expansion of accidents.

At present, in the fault diagnosis process of transformers, the conventional method is to collect operating parameters in a centralized manner, and judge whether the transformer is faulty by collecting the operating parameters in a centralized manner; for this method, the diagnosis process is not only cumbersome, but also slow, which easily takes up a lot of diagnostic time. The fault information is hysteresis, which is not conducive to the safe operation of the transformer.

Contents of the invention

Embodiments of the present invention provide a multi-dimensional transformer fault diagnosis and state evaluation system and method, which aims to solve the existing problem of centralized collection of operating parameters and centralized determination of whether the transformer has failed, resulting in lagging fault information.

In view of the problems referred to above, the technical solution proposed by the present invention is:

In the first aspect, a multi-dimensional transformer fault diagnosis and state evaluation system, the fault diagnosis and state evaluation system includes:

A receiving module, which is used to receive the historical operating parameters of the transformer uploaded by the user and analyze the frequency of fault characteristic parameters;

A classification module, which is used to receive the current operating parameters of the transformer and extract the characteristic parameters of suspected categories;

A diagnosis module, which is used to compare the frequency of the suspected category characteristic parameter with the fault characteristic parameter to determine the fault category characteristic parameter;

An analysis module, which is used to determine the fault level of the fault category characteristic parameter;

An evaluation module, which is used to determine the fault type and credible health state value of the fault category characteristic parameter, and generate a visual chart of the fault type, the fault level and the credible health state value.

As a preferred technical solution of the present invention, the receiving module includes:

An identification unit, configured to identify fault characteristic parameters in the historical operating parameters after receiving the historical operating parameters of the transformer uploaded by the user;

A statistical unit, which is used to count the fault frequency of the fault characteristic parameter to obtain the frequency of the fault characteristic parameter;

The first labeling unit is configured to create a first semantic keyword for the frequency of the fault characteristic parameter, mark the frequency of the fault characteristic parameter with a grade label, and associate the first semantic keyword with the grade label.

As a preferred technical solution of the present invention, the historical operating parameters include voltage parameters, current parameters, oil temperature parameters, chromatography parameters, sound wave parameters, vibration parameters and temperature parameters.

As a preferred technical solution of the present invention, the classification module includes:

A receiving unit, which is used to receive the current operating parameters of the transformer and classify them to obtain initial category characteristic parameters;

A first judging unit, configured to judge whether the initial category feature parameter is within its corresponding preset threshold interval.

As a preferred technical solution of the present invention, the classification module further includes a creation unit, which is used to extract the suspected category feature parameters and create a second category feature parameter if the initial category feature parameter is outside its corresponding preset threshold interval Semantic keywords.

As a preferred technical solution of the present invention, the diagnostic module includes:

A first comparing unit, which is used to compare the second semantic keyword with the first semantic keyword to obtain a first comparison similarity;

A second comparison unit, configured to compare the suspected category feature parameter with the frequency of the fault feature parameter based on the first comparison similarity to obtain a second comparison similarity;

a processing unit, configured to apply a first weight to the second comparison similarity to obtain a first confidence;

A second judging unit, configured to judge whether the first confidence level is within a first preset confidence interval, and if so, determine the suspected category characteristic parameter as a fault category characteristic parameter.

As a preferred technical solution of the present invention, the analysis module includes:

A third comparing unit, which is used to compare the characteristic parameters of the fault category with their corresponding characteristic parameter thresholds to obtain a comparison result;

A first determining unit, configured to determine the fault level of the fault category characteristic parameter according to the comparison result.

As a preferred technical solution of the present invention, the evaluation module includes:

a calculation unit, which is used to calculate the initial health state value of the fault category characteristic parameter based on the fault level;

A weighting unit, which is used to give weight to the initial health status value based on the grade label to obtain a comprehensive health status value;

A third judging unit, configured to judge whether the comprehensive health state value is within a second preset confidence interval.

As a preferred technical solution of the present invention, the evaluation module further includes a second determination unit, which is used to determine the fault type according to the fault category characteristic parameters, if the comprehensive health state value is within the second preset confidence interval , then determine the comprehensive health status value as a credible health status value, and generate a visual chart for the fault type, the fault level, and the credible health status value.

In a second aspect, an embodiment of the present invention provides a multi-dimensional transformer fault diagnosis and state assessment method, the fault diagnosis and state assessment method includes the following steps:

6. Receive the historical operating parameters of the transformer uploaded by the user and analyze the frequency of fault characteristic parameters;

6. Receive the current operating parameters of the transformer and extract the characteristic parameters of the suspected category;

6. Compare the frequency of the suspected category characteristic parameter with the fault characteristic parameter to determine the fault category characteristic parameter;

6. Determine the fault level of the fault category characteristic parameter;

6. Determine the fault type and credible health state value of the fault category characteristic parameter, and generate a visual chart for the fault type, the fault level, and the credible health state value.

The beneficial effects of the above-mentioned technical solutions provided by the embodiments of the present invention at least include:

() The current operating parameters of the present invention are sorted in advance to obtain the characteristic parameters of the suspected category, and then the characteristic parameters of the suspected category are diagnosed, and after weighting processing, the characteristic parameters of the fault category can be further determined, which can be used as a basis for judging whether there is a fault , not only can speed up the diagnosis, but also can guarantee the accuracy of diagnosis.

() The present invention comprehensively evaluates the health state of the transformer, thereby being able to comprehensively evaluate the faults of the transformer, so that maintenance personnel can formulate corresponding maintenance measures.

The above description is only an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the description, and in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable , the specific embodiments of the present invention are enumerated below.

Description of drawings

Fig. 1 is a structural schematic diagram of a multi-dimensional transformer fault diagnosis and state evaluation system disclosed by the present invention;

Fig. 2 is a flowchart of a multi-dimensional transformer fault diagnosis and state evaluation method disclosed in the present invention.

Explanation of reference signs: 100, receiving module; 110, identification unit; 120, statistical unit; 130, first label unit; 200, classification module; 210, receiving unit; 220, first judgment unit; 230, creation unit; 300 , diagnosis module; 310, first comparison unit; 320, second comparison unit; 330, processing unit; 340, second judgment unit; 400, analysis module; 410, third comparison unit; 420, first determination unit; 500, evaluation module; 510, calculation unit; 520, weighting unit; 530, third judgment unit; 540, second determination unit.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is some embodiments of the present invention, but not all of them. Based on the implementation manners in the present invention, all other implementation manners obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the implementation manners in the present invention, all other implementation manners obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

It should be noted that like numerals and letters denote similar items in the following figures, therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures.

Embodiment one

Referring to the accompanying drawings, the present invention provides a technical solution: a multi-dimensional transformer fault diagnosis and state evaluation system, the fault diagnosis and state evaluation system includes:

A receiving module, which is used to receive the historical operating parameters of the transformer uploaded by the user and analyze the frequency of fault characteristic parameters;

A classification module, which is used to receive the current operating parameters of the transformer and extract the characteristic parameters of suspected categories;

A diagnosis module, which is used to compare the frequency of the suspected category characteristic parameter with the fault characteristic parameter to determine the fault category characteristic parameter;

An analysis module, which is used to determine the fault level of the fault category characteristic parameter;

An evaluation module, which is used to determine the fault type and credible health state value of the fault category characteristic parameter, and generate a visual chart of the fault type, the fault level and the credible health state value.

Specifically, the present application first conducts statistics based on historical operating parameters to obtain multiple fault characteristic parameter frequencies and archives them; wherein, one fault type corresponds to one fault characteristic parameter frequency. Classify the current operating parameters transmitted by the transformer each time, and screen out the suspected category characteristic parameters; that is, sort the current operating parameters in advance, which can avoid repeated comparisons and increase the speed of comparisons.

Next, diagnose the characteristic parameters of the suspected category, that is, compare the frequency of the characteristic parameters of the suspected category with the fault characteristic parameters of the same type of fault, and after weighting processing, the characteristic parameters of the fault category can be further determined, which can be used as a basis for judging whether there is a fault. basis, so as to ensure the accuracy of diagnosis.

Finally, based on historical factors, the health status of the transformer can be comprehensively judged, that is, based on the characteristic parameters of the fault category and the frequency of the fault characteristic parameters of the same type of fault, the credible health status value can be finally obtained.

Further, the receiving module includes:

An identification unit, which is used to identify fault characteristic parameters in the historical operating parameters after receiving the historical operating parameters of the transformer uploaded by the user; wherein the historical operating parameters include voltage parameters, current parameters, oil temperature parameters, Chromatographic parameters, acoustic parameters, vibration parameters and temperature parameters.

A statistical unit, which is used to count the fault frequency of the fault characteristic parameter to obtain the frequency of the fault characteristic parameter;

The first labeling unit is configured to create a first semantic keyword for the frequency of the fault characteristic parameter, mark the frequency of the fault characteristic parameter with a grade label, and associate the first semantic keyword with the grade label.

Specifically, seven different types of characteristic parameters are included in the historical operating parameters, and each type has a corresponding fault characteristic parameter; for example, a voltage parameter has a fault characteristic parameter indicating its own fault. The frequency of fault characteristic parameters refers to the total number of failures of each type of fault characteristic parameters. In the process of labeling grade labels, according to the preset rules, according to which frequency interval the fault characteristic parameter frequency is in, labels with green, yellow and red colors are given respectively; for example, the green label corresponds to the first level, the yellow label corresponds to the second level and the red label Corresponds to level three.

Further, the classification module includes:

A receiving unit, which is used to receive the current operating parameters of the transformer and classify them to obtain initial category characteristic parameters;

A first judging unit, which is used to judge whether the initial category feature parameter is within its corresponding preset threshold interval;

The creating unit is configured to extract the suspected category feature parameter and create the second semantic keyword if the initial category feature parameter is outside its corresponding preset threshold interval.

Specifically, there are also seven initial category characteristic parameters, which correspond to the seven types of historical operating parameters one by one. According to the current operating parameters transmitted by the transformer, the initial category characteristic parameters are analyzed one by one, and the suspected category characteristic parameters are finally determined. Therefore, real-time monitoring can be realized, and the maintenance efficiency of the transformer can be improved.

Further, the diagnostic module includes:

A first comparing unit, which is used to compare the second semantic keyword with the first semantic keyword to obtain a first comparison similarity;

A second comparison unit, configured to compare the suspected category feature parameter with the frequency of the fault feature parameter based on the first comparison similarity to obtain a second comparison similarity;

a processing unit, configured to apply a first weight to the second comparison similarity to obtain a first confidence;

A second judging unit, configured to judge whether the first confidence level is within a first preset confidence interval, and if so, determine the suspected category characteristic parameter as a fault category characteristic parameter.

Specifically, the second comparison can only be performed after the similarity of the first comparison meets the requirements, which not only avoids errors in fault comparison, but also saves comparison time and improves the monitoring ability of transformers. At the same time, by weighting the similarity of the second comparison, it is ensured that the similarity of the second comparison is more credible, so that the fault judgment of the transformer is more accurate.

Further, the analysis module includes:

A third comparing unit, which is used to compare the characteristic parameters of the fault category with their corresponding characteristic parameter thresholds to obtain a comparison result;

A first determining unit, configured to determine the fault level of the fault category characteristic parameter according to the comparison result.

Specifically, each type of feature parameter threshold has a different threshold interval, and the threshold intervals increase in order and correspond to the fault level one by one; by comparing the results to determine which threshold interval it falls into, the specific threshold interval can be finally determined. The failure level of the system provides convenience for health status assessment.

Further, the evaluation module includes:

a calculation unit, which is used to calculate the initial health state value of the fault category characteristic parameter based on the fault level;

A weighting unit, which is used to give weight to the initial health status value based on the grade label to obtain a comprehensive health status value;

A third judging unit, which is used to judge whether the comprehensive health state value is within the second preset confidence interval;

A second determining unit, configured to determine a fault type according to the characteristic parameter of the fault category, and if the comprehensive health state value is within a second preset confidence interval, then determine the comprehensive health state value as a credible health state value , and generate a visual chart of the fault type, the fault level, and the trusted health status value.

Specifically, an initial health state value is first determined according to the fault level, and then a comprehensive health state value is determined by combining the historical level labels, so that the fault of the transformer can be comprehensively evaluated, so that maintenance personnel can formulate corresponding maintenance measures; at the same time, The integrated health status value is further processed to make the integrated health status value more credible.

Above, it should be noted that both the first semantic keyword and the second semantic keyword refer to semantic keywords or semantic keywords.

Embodiment two

The embodiment of the present invention also discloses a multi-dimensional transformer fault diagnosis and state assessment method. Referring to the accompanying drawings, the fault diagnosis and state assessment method includes the following steps:

6. Receive the historical operating parameters of the transformer uploaded by the user and analyze the frequency of fault characteristic parameters;

6. Receive the current operating parameters of the transformer and extract the characteristic parameters of the suspected category;

6. Compare the frequency of the suspected category characteristic parameter with the fault characteristic parameter to determine the fault category characteristic parameter;

6. Determine the fault level of the fault category characteristic parameter;

6. Determine the fault type and credible health state value of the fault category characteristic parameter, and generate a visual chart for the fault type, the fault level, and the credible health state value.

It is understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy described.

In the foregoing Detailed Description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, the invention lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby expressly incorporated into the Detailed Description, with each claim standing on its own as a separate preferred embodiment of this invention.

Those skilled in the art should also understand that various illustrative logical blocks, modules, circuits and algorithm steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software or a combination thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a memory, a flash memory, a memory of 520, a memory of 3520, a memory of 3520, a register, a hard disk, a removable disk, 520 or any other form of storage medium known in the art. An exemplary The storage medium is connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located at In $6,&. The $6,& may be located in the user terminal. Certainly, the processor and the storage medium may also exist in the user terminal as discrete components.

For software implementation, the techniques described in this application can be implemented with modules, such as procedures, functions, and so on, that perform the functions described herein. These software codes can be stored in memory units and executed by processors. The memory unit can be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.

The foregoing description includes illustrations of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods to describe the above-mentioned embodiments, but those skilled in the art should recognize that various embodiments can be further combined and permuted. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "comprises" is used in the specification or claims, the word is encompassed in a manner similar to the term "comprises" as interpreted when "comprises" is used as a link in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".

Claims (10)

1. A multi-dimensional transformer fault diagnosis and condition assessment system, comprising:

the receiving module is used for receiving historical operating parameters of the transformer uploaded by a user and analyzing the frequency of the fault characteristic parameters;

the classification module is used for receiving the current operation parameters of the transformer and extracting suspected class characteristic parameters;

the diagnosis module is used for comparing the suspected category characteristic parameters with the frequency of the fault characteristic parameters to determine fault category characteristic parameters;

the analysis module is used for determining the fault grade of the fault category characteristic parameter;

and the evaluation module is used for determining the fault type and the credible health state value of the fault category characteristic parameter and generating a visual chart from the fault type, the fault grade and the credible health state value.

2. The multi-dimensional transformer fault diagnosis and condition evaluation system of claim 1, wherein the receiving module comprises:

the identification unit is used for identifying fault characteristic parameters in historical operating parameters of the transformer after receiving the historical operating parameters uploaded by a user;

the statistical unit is used for counting the fault frequency of the fault characteristic parameters to obtain the frequency of the fault characteristic parameters;

and the first label unit is used for creating a first semantic keyword/word for the fault characteristic parameter frequency, marking the fault characteristic parameter frequency with a grade label, and associating the first semantic keyword/word with the grade label.

3. The multi-dimensional transformer fault diagnosis and condition assessment system according to claim 1 or 2, wherein said historical operating parameters comprise voltage parameters, current parameters, oil temperature parameters, chromatographic parameters, sonic parameters, vibration parameters and temperature parameters.

4. The system of claim 2, wherein the classification module comprises:

the receiving unit is used for receiving the current operation parameters of the transformer and classifying the current operation parameters to obtain initial class characteristic parameters;

and the first judging unit is used for judging whether the initial category characteristic parameters are within the corresponding preset threshold value interval.

5. The system according to claim 4, wherein the classification module further comprises a creation unit, configured to extract a suspected class feature parameter and create a second semantic keyword/word if the initial class feature parameter is outside of a preset threshold interval corresponding to the initial class feature parameter.

6. The multidimensional transformer fault diagnosis and condition assessment system according to claim 5, wherein the diagnosis module comprises:

the first comparison unit is used for comparing the second semantic keyword/word with the first semantic keyword/word to obtain a first comparison similarity;

the second comparison unit is used for comparing the suspected category characteristic parameters with the fault characteristic parameter frequency based on the first comparison similarity to obtain a second comparison similarity;

the processing unit is used for applying a first weight to the second comparison similarity to obtain a first confidence coefficient;

and the second judging unit is used for judging whether the first confidence coefficient is in a first preset confidence interval or not, and if so, determining the suspected type characteristic parameter as a fault type characteristic parameter.

7. The multi-dimensional transformer fault diagnosis and condition assessment system according to claim 6, wherein said analysis module comprises:

the third comparison unit is used for comparing the fault category characteristic parameters with the corresponding characteristic parameter threshold values to obtain comparison results;

and the first determining unit is used for determining the fault grade of the fault class characteristic parameter according to the comparison result.

8. The multidimensional transformer fault diagnosis and condition assessment system according to claim 7, wherein the assessment module comprises:

a calculating unit for calculating an initial state of health value of the fault category characteristic parameter based on the fault level;

a weighting unit for giving a weight to the initial health state value based on the grade label to obtain a comprehensive health state value;

and the third judging unit is used for judging whether the comprehensive health state value is in a second preset confidence interval or not.

9. The multidimensional transformer fault diagnosis and condition evaluation system of claim 8, wherein the evaluation module further comprises a second determination unit, which is configured to determine a fault type according to the fault category characteristic parameter, determine the integrated health state value as a trusted health state value if the integrated health state value is within a second preset confidence interval, and generate a visual chart from the fault type, the fault level and the trusted health state value.

10. A multi-dimensional transformer fault diagnosis and condition evaluation method applied to the multi-dimensional transformer fault diagnosis and condition evaluation system of any one of claims 1 to 9, wherein the fault diagnosis and condition evaluation method comprises the following steps:

s1, receiving historical operating parameters of the transformer uploaded by a user and analyzing frequency of fault characteristic parameters;

s2, receiving the current operation parameters of the transformer and extracting suspected class characteristic parameters;

s3, comparing the suspected type characteristic parameters with the frequency of the fault characteristic parameters to determine fault type characteristic parameters;

s4, determining the fault grade of the fault category characteristic parameter;

and S5, determining the fault type and the credible health state value of the fault category characteristic parameter, and generating a visual chart by using the fault type, the fault grade and the credible health state value.

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