CN113722659B - Fault mode-based substation main equipment diagnosis method and system - Google Patents
Fault mode-based substation main equipment diagnosis method and system Download PDFInfo
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- G01R31/327—Testing of circuit interrupters, switches or circuit-breakers
- G01R31/3277—Testing of circuit interrupters, switches or circuit-breakers of low voltage devices, e.g. domestic or industrial devices, such as motor protections, relays, rotation switches
- G01R31/3278—Testing of circuit interrupters, switches or circuit-breakers of low voltage devices, e.g. domestic or industrial devices, such as motor protections, relays, rotation switches of relays, solenoids or reed switches
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- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/62—Testing of transformers
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Abstract
The invention relates to a method and a system for diagnosing power transformation main equipment based on a defect mode. The method adopts a defect mode-based equipment defect diagnosis method, defines a defect criterion based on a detection project by defining the defect mode of the main power transformation equipment, defines an analysis matrix and a defect probability comprehensive algorithm, carries out qualitative analysis on the defects, and effectively realizes the defect diagnosis of the main power transformation equipment; and the defects are quantitatively analyzed by calculating the severity of the state quantity, and meanwhile, a data basis is provided for developing state evaluation and risk assessment.
Description
Technical Field
The invention relates to the technical field of equipment diagnosis, in particular to the technical field of power transformation main equipment diagnosis based on a defect mode.
Background
The defect diagnosis is the precondition of equipment maintenance and is the guarantee of stable operation of electrical equipment. The basis for defect diagnosis is derived from the state detection of the device. The state detection can be obtained through inspection information, operation maintenance information, conventional electrical tests, infrared temperature measurement, protection device action information and application of various online monitoring devices. The current defect diagnosis methods are mainly classified into the following 3 categories.
1) Conventional defect diagnosis method
The conventional defect diagnosis method mainly comprises a post diagnosis method and a pre diagnosis method. The post-diagnosis is performed when a defect occurs. Defects are directly detected according to various physical and chemical means. For example: and when the GIS equipment has a defect, the appearance of the equipment is checked on site, the action report of the smart protection device is analyzed, and the gas is analyzed. The method is quick, simple and effective.
The prior diagnosis is performed before the occurrence of the defect. And analyzing and comparing the data of the electrical equipment in normal operation to find out the defect development trend. The method is based on historical data, compares the equipment parameters, maintenance and operation data provided by manufacturers, analyzes the development tendency of the equipment state and obtains a diagnosis result, and the method is based on the defects and repeatedly summarizes rules.
2) Intelligent defect diagnosis method
With the maturity of artificial intelligence technology, various intelligent defect diagnosis systems have been widely applied in power systems, such as: the inspection of a transformer substation robot, an online monitoring system of transformer oil chromatography, an online monitoring device of leakage current of a lightning arrester, a deformation monitoring system of a transformer winding and the like become main diagnosis means of defects of transformer substation equipment.
3) Mathematical method defect diagnosis method
Mathematical models of equipment defects are established by mathematical means such as mathematical statistics, neural networks, various algorithms and the like, various defect information is screened, defect rules are summarized by methods such as deduction and induction, defect symbols are deduced reversely, and then the defect symbols and field actual data are verified to form an effective defect diagnosis method.
The power transformation main equipment comprises electric equipment such as a transformer, a reactor, a combined electrical apparatus, a current transformer, a voltage transformer, a lightning arrester, an isolating switch, a circuit breaker and the like. The operation state of the main power transformation equipment plays a decisive role in the safety and stability of the operation of the transformer substation, and the defect diagnosis of the main power transformation equipment is a subject of constant exploration in the technical field.
At present, the state perception capability, data dimensionality and historical data integrity of the main power transformation equipment have obvious difference, which is mainly shown in that 1, partial equipment lacks effective technical means such as online monitoring and live detection, and cannot be substantially improved in a short period. A portion of the available dimensional data is missing at the time of state analysis. 2. The method is influenced by various factors, the integrity of historical state data of part of equipment is poor, and state data for longitudinal comparison is lacked during state analysis. 3. The online monitoring data has good timeliness but poor data quality, and the offline detection data has poor timeliness but good data quality. There are differences in data availability for each dimension.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for diagnosing the power transformation main equipment based on the defect mode.
The invention adopts the following technical scheme:
a method for diagnosing a substation main device based on a defect mode comprises the following steps:
s1, constructing a defect diagnosis model:
s11, defining a defect mode, and defining typical defect types of the power transformation main equipment;
s12, defining detection items, and carrying out category grouping on all state information of the power transformation main equipment under the existing conditions according to the source of the state information and the incidence relation between the state information and a defect mode to form the detection items for judging whether the detection is qualified or not;
s13, defining an analysis matrix, determining the corresponding relation between the detection items and the defect mode, and assigning each corresponding relation to a basic defect probability;
s14, defining a defect probability comprehensive algorithm for comprehensively calculating the defect probability of a plurality of unqualified detection items related to one defect;
s2, model application:
s21, judging detection items, namely inputting full-dimensional data into the detection items, judging whether the input data is abnormal or not through the detection items, and screening out unqualified detection items;
s22, matching the defect modes to obtain a defect mode corresponding to each unqualified detection item and corresponding defect probability;
s23, defect probability synthesis and severity analysis, which comprises the following parallel steps:
s231, defect probability synthesis, namely performing comprehensive calculation on the defect mode and the basic defect probability of the unqualified detection item corresponding to the defect mode;
s232, analyzing and calculating the severity, namely a quantitative index used for representing the defect/defect development degree in the model;
and S24, outputting a diagnosis result, and outputting recommended measures based on the calculation result of the step S23.
Further, the step S13 specifically includes: the analysis matrix is a D multiplied by M matrix, wherein D is a defect set, M is a detection item set, a defect probability exists in a defect mode reflected by each detection item, the defect probability value is a decimal between 0 and 1, and the numerical value is a statistical result of empirical data and/or an empirical value provided by an expert.
Further, the step S21 specifically includes: inputting full-dimensional data into detection items to form three types of detection item sets:
1) The detection item set without parameters, and the data has no access model or detection means;
2) A qualified test item set with parameters;
3) A set of disqualified test items with parameters.
Further, the step S22 specifically includes:
the detection items judged to be parameter-free detection items and qualified detection items with parameters in the rejection detection item judgment stage cannot participate in defect diagnosis of equipment, and invalid data dimensionality is actively reduced;
and performing association retrieval on the unqualified detection items with the parameter judgment result in the detection item judgment stage by combining the analysis matrix to obtain a defect mode corresponding to each unqualified detection item and a corresponding defect probability.
Further, the step S231 specifically includes: traversing the analysis result of defect mode matching, carrying out comprehensive calculation on the defect mode and the basic defect probability of a plurality of unqualified detection items corresponding to a certain defect mode, and outputting the unique defect probability of each defect mode and the integral defect probability of the equipment; the step S232 specifically includes: and according to the comparison between the test measurement value and the standard value in the same item or related procedures in the handover test table, mapping the ratio to the [0,1] interval.
The invention also includes a system for diagnosing a substation master based on a fault mode, the system comprising:
the model building module is used for building a defect model;
the model application module is used for carrying out defect diagnosis;
the model building module specifically comprises:
the defect mode defining unit is used for defining a defect mode and defining a typical defect type of the power transformation main equipment;
the detection item definition unit is used for defining detection items, and carrying out category grouping on all state information under the existing conditions of the power transformation main equipment according to the source of the state information and the incidence relation between the state information and the defect mode to form a detection item for judging whether the detection is qualified or not;
the analysis matrix definition unit is used for defining an analysis matrix, determining the corresponding relation between the detection items and the defect mode and assigning each corresponding relation to a basic defect probability;
the defect probability comprehensive algorithm definition unit is used for defining a defect probability comprehensive algorithm and comprehensively calculating the credibility of a plurality of unqualified detection items related to one defect;
the model application module specifically comprises:
the detection item judgment unit is used for inputting the full-dimensional data into a detection item, judging whether the input data is abnormal or not through the detection item, and screening out unqualified detection items;
the defect mode matching unit is used for obtaining a defect mode corresponding to each unqualified detection item and corresponding defect probability;
the defect probability synthesis and severity analysis unit comprises: the defect probability comprehensive subunit is used for comprehensively calculating the defect mode and the basic defect probability of the unqualified detection item corresponding to the defect mode; the severity analysis subunit is used for analyzing and calculating the severity, namely a quantitative index used for representing the defect/defect development degree in the model;
and the diagnosis result output unit outputs recommended measures based on the calculation results of the defect probability integration and severity analysis unit.
Further, the analysis matrix definition unit is specifically configured to: and defining an analysis matrix as a D multiplied by M matrix, wherein D is a defect set, M is a detection item set, a defect probability exists in a defect mode reflected by each detection item, the defect probability value is a decimal between 0 and 1, and the value of the defect probability value is a statistical result of empirical data and/or an empirical value provided by an expert.
Further, the detection item determination unit is specifically configured to: inputting full-dimensional data into detection items to form three types of detection item sets:
1) The detection item set without parameters, and the data has no access model or detection means;
2) A set of qualified test items with parameters;
3) A set of disqualified test items with parameters.
Further, the defect pattern matching unit is specifically configured to:
the detection items judged to be parameter-free detection items and qualified detection items with parameters in the rejection detection item judgment stage cannot participate in the analysis of equipment defects, and invalid data dimensions are actively reduced;
and performing association retrieval on the unqualified detection items with the parameter judgment result in the detection item judgment stage by combining the analysis matrix to obtain a defect mode corresponding to each unqualified detection item and a corresponding defect probability.
Further, the defect probability integration subunit is specifically configured to: traversing the analysis result of defect mode matching, carrying out comprehensive calculation on the defect mode and the basic defect probability of a plurality of unqualified detection items corresponding to a certain defect mode, and outputting the unique defect probability of each defect mode and the integral defect probability of the equipment; the severity analysis subunit is specifically configured to: and mapping the ratio to the [0,1] interval according to the comparison between the test measurement value and the standard value in the same item or related procedures in the handover test table.
The invention achieves the following beneficial effects: influence factors of data quality and data type difference are considered in the corresponding relation between the detection items and the defect modes, so that the influence caused by the data quality and the data type difference is solved.
The defect diagnosis model can diagnose the defect types and the defect components, and can provide a mathematical analysis basis for the defect development and evolution process, other potential hidden dangers possibly existing in the power transformation main equipment and the prediction of defects possibly occurring in the future; the defect diagnosis model can also calculate the severity of the state quantity corresponding to the defect mode, and provides a data basis for equipment state evaluation.
Drawings
Fig. 1 is a schematic structural diagram of a defect diagnosis model according to the present invention.
FIG. 2 is a schematic diagram of an analysis matrix according to the present invention.
FIG. 3 is a schematic diagram of the diagnostic analysis process of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby. It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure.
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 according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The invention simulates the intelligent reasoning process idea of applying symptom phenomena/data to diagnose the defects of the main equipment of the power transformation by human experts. Example 1 of the present invention comprises the following method steps:
s1, constructing a defect model, which comprises the following steps:
s11, defining a defect mode: and defining typical defect/defect type of the transformer master equipment, namely a defect mode.
To accurately define the defect mode, the power transformation master needs to be further divided into various sub-devices/sub-systems. The division of the subsystems can adopt two methods: functional methods and component methods. The functional method is to divide the main equipment of the transformer according to the function of the component, such as SF 6 The circuit breaker is divided into an insulation system, a breaking system, a current carrying system, an SF 6 Gas, mechanical systems, etc. The component rule divides the substation main equipment according to the physical position of the component.
The sub-device/sub-system defect mode includes a sub-device/sub-system functional failure mode and a defect mode, wherein failure refers to the loss of function of a component, and defect is a component abnormality but not necessarily loss of function, wherein the functional failure mode may cause the loss of function of the component, and the defect mode is that the component is abnormal but not necessarily causes the loss of function, and can only maintain operation for a certain time.
Still at SF 6 The circuit breaker is taken as an example, and the defect mode is shown in table 1 below.
TABLE 1 Defect modes of SF6 circuit breakers
Each defect pattern may be further divided into a plurality of sub-defect patterns. The sub-defect modes are shown in table 2 below, taking a transformer as an example.
TABLE 2 partial Defect mode of transformers
S12, defining detection items: all state information under the existing condition of the power transformation main equipment is classified and grouped according to the source and the incidence relation with the defect mode, and the grouped data is logically and mathematically combined by combining threshold values, trends, correlation regulations and expert experiences to form the exclusive criterion of the minimum unit, namely a detection item for judging whether the detection is qualified.
Taking a transformer as an example, the test items are shown in table 3 below.
TABLE 3 partial inspection items of transformers
S13, defining an analysis matrix: determining the corresponding relation between the detection items and the defect mode according to typical defect cases and experience, endowing each corresponding relation with a basic defect probability according to different capabilities of the detection items for representing the defect mode, and constructing a top-level analysis matrix with many-to-many detection items and defect modes by taking the basic defect probability as a main line. The analysis matrix is a D × M matrix, where D is the set of defects and M is the set of defect symptoms or the set of inspection items. According to typical defect cases and experiences, the corresponding relations between the detection items and the defect modes are determined, and the capabilities of characterizing the defect modes according to the detection items are different, so that the situation that positive correlation and negative correlation exist between defects and symptoms in the analysis matrix is analyzed, each corresponding relation is given a basic defect probability, and the basic defect probability is used as a main line to construct a top-level analysis matrix with many pairs of detection items and defect modes, as shown in fig. 2. In the diagnosis model, a confidence coefficient or a defect probability exists in a defect mode reflected by each detection item, the defect probability value is a decimal between 0 and 1, and the numerical value is a statistical result of empirical data and an empirical value provided by an expert. Taking a transformer as an example, the analysis matrix is shown in the following table 4
TABLE 4 partial analysis matrix of transformers
S14, defining a defect probability comprehensive algorithm: and defining a defect probability comprehensive algorithm, and reasonably changing the output defect probability when the directivity criterion of a certain defect mode is increased. In the defect diagnosis process, if a plurality of unqualified detection items correspond to a certain defect, the credibility of the evidences needs to be integrated. In order to solve the comprehensive problem of confidence, the following method is adopted:
in the confidence method, the diagnosis knowledge is expressed by a production rule for an arbitrary inference rule k ij The standard form is as follows: IF m j THEN d i WITH CF(d i |m j )
Wherein m is j Represents evidence, d i Indicating a defect conclusion, confidence CF (d) i |m j ) Is shown in evidence m j Assuming defect d in the presence i To the extent of confidence.
From conditional probability and prior probability, CF (d) i |m j ) Is defined as follows:
when a diagnosis model is actually established, the CF assignment method is flexible, and the statistical result of empirical data can be adopted, and the empirical value provided by experts can be used for replacing the CF assignment method.
The confidence function CF should satisfy the following basic properties:
(1).-1≤CF(d i |m j )≤+1，CF<0 represents the degree of distrust for the proposition.
(2) If d 1 ,d 2 ,L,d m Are mutually independent defect hypotheses, m j For evidence of their benefits, then
(3) If m 2 For unknown evidence, then CF (d) i |m 1 ∧m 2 )＝CF(d i |m 1 )。
In the defect diagnosis process, if there are a plurality of evidences supporting or negating D = { D = { (D) } 1 ,d 2 ,L,d m Some defect d in i It is desirable to integrate the trustworthiness of these proofs. Suppose there are two rules
IF m 1 THEN d i WITH CF(d i |m 1 )
IF m 2 THEN d i WITH CF(d i |m 2 )
The associated composite confidence is defined as
Wherein, CF (d) i |m 1 ∧m 2 ) Representing a comprehensive credibility value, namely the defect probability after the comprehensive; and X and Y are defect probabilities corresponding to the criterion pointing to the defect mode.
The following aspects are mainly considered for the operation experience of the power transformation equipment: whether the equipment is subjected to outlet short circuits, overvoltage surges, long-term overloads, and lengthy downtime, etc. The possible effects of these operational experiences on the equipment can be introduced into the diagnostic reasoning process in the form of weighting the confidence in the defects.
The reliability correction formula is
CF * ＝(1+λ)CF
Wherein λ is artificially set according to the operation experience of the power transformation equipment.
And establishing a defect diagnosis model by defining a defect mode, defining defect criteria, defining an analysis matrix and defining a defect comprehensive algorithm.
The fault diagnosis model of the substation main equipment is established on the basis of the early-stage basic research. And (4) determining the characteristic state quantity set, the typical defect mode set and the incidence relation of each item in the two sets to construct a main framework of the model. The main logic in the model comprises three parts of defect identification, defect confidence analysis (qualitative) and defect severity analysis (quantitative). The input design for the model is: all characteristic state quantities which can be collected; the output design is as follows: possible defect modes of the device, confidence and severity of individual defects. The main logical structure of the defect diagnosis model of the present invention is shown in fig. 1.
And S2, applying the model. According to the established defect reasoning idea, the system divides the diagnosis and analysis process of the model into three stages: the first stage, detecting item judgment; in the second stage, defect mode matching is carried out; and in the third stage, integrating the defect probability, wherein the specific process is shown in the attached figure 3.
And S21, judging detection items, namely inputting full-dimensional data into the detection items, rapidly analyzing the detection items, judging whether the input data is abnormal or not, and screening out unqualified detection items, namely equipment abnormal states, so that data dimensions can be reduced, and defect diagnosis rate and precision can be improved. Each detection item is a judgment formula formed by applying a mathematical relationship or a logical relationship to one or more state quantities. And extracting and analyzing the input experimental data, online monitoring data, charged detection data, inspection data, operation condition data, load, environment and other data, and transmitting the data into corresponding detection project models to obtain a conclusion whether the detection projects are qualified or not. Three types of detection item sets are formed at this stage:
1) And the data has no access model or detection means.
2) A collection of qualified test items with parameters.
3) A set of disqualified test items with parameters.
S22, defect pattern matching: when defect analysis is carried out, the system firstly rejects the detection items judged to be parameter-free and qualified detection items judged to be parameter-free in the detection item judgment stage, so that the detection items cannot participate in the analysis of the equipment defects, namely, invalid data dimensionality is actively reduced. And then, performing correlation retrieval on the unqualified detection items with the parameter judgment result in the detection item judgment stage by combining a top layer analysis model (matrix) to obtain a defect mode corresponding to each unqualified detection item and a corresponding defect probability.
S23, defect probability synthesis and severity analysis, which comprises the following parallel steps:
and S231, integrating the defect probability, and comprehensively calculating the defect mode and the basic defect probability of the unqualified detection item corresponding to the defect mode. Traversing the analysis result of the defect mode matching, if a plurality of unqualified detection items correspond to a certain defect, comprehensively calculating the probability of the basic defect of the defect mode and the detection items. Finally realizing defect probability calculation unique to each defect mode and equipment overall defect probability output
And S232, analyzing and calculating the severity, namely a quantitative index used for representing the defect/defect development degree in the model. The severity value of a defect is a quantitative indicator used in the model to characterize the defect/degree of defect development. The parameters or sets of parameters that can characterize the severity are different for each defect. Aiming at the characteristics, in the process of carrying out quantitative analysis on the defect modes, a differentiated severity analysis formula is formed aiming at the typical defect mode set. And mapping the ratio to the [0,1] interval according to the comparison between the test measurement value and the standard value in the same item or related procedures in the handover test table. The mapping rule is not general, and each defect is different from the mapping rule of the corresponding data.
In the model processing process, after the defect mode is determined and the severity value is output, the calculation of the severity is mainly applied to a defect prediction and state evaluation model.
And S24, outputting a diagnosis result, and outputting recommended measures based on the calculation result of the step S23.
The present invention in embodiment 2 will be described by taking the diagnosis of the discharge defect of the transformer as an example.
In this embodiment, the online monitoring and early warning system sends out early warning according to the abnormal growth trend of two characteristic gases in the online monitoring data to trigger defect diagnosis, and provides recommended measures to perform offline oil chromatographic analysis as soon as possible.
A defect pattern and detection items are first defined. For the transformer discharge defect of this example 2, the defect mode relates to an insulating oil defect, and the sub-defect mode is a partial discharge in oil.
And in the defect diagnosis process, firstly, judging detection items, then, carrying out defect mode matching, and eliminating non-parameter detection items and qualified detection items with parameters, thereby reducing data dimensionality and improving the operation speed of the diagnosis model.
Then, defect diagnosis is carried out according to the analysis data of the gas in the off-line oil, and the defect result is the discharge defect CF (confidence) =0.99; wherein high energy discharge CF =0.4, low energy discharge CF =0.6 and no solid insulation is involved CF =0.8.
The detection items also comprise off-line oil gas analysis data, iron core and clamp grounding current data, high-frequency partial discharge detection data in live detection, ultrasonic signal detection data and ultrahigh frequency detection data input. And the off-line analysis of gas in the oil, the ultrasonic signal detection and the ultrahigh frequency detection are unqualified.
And inputting the defect types of the diagnosis results into a state evaluation and result output unit, giving recommended measures by the state evaluation and result output unit, carrying out tests according to the measures by operation and maintenance personnel, supplementing data to the defect diagnosis model, and diagnosing the defect diagnosis model again according to the newly supplemented data. The second defect diagnosis result is a discharge defect CF (confidence) =0.99; wherein, high energy discharge CF =0.4, low energy discharge CF =0.6, no solid insulation involved CF =0.8 and suspension discharge CF =0.7. The third defect diagnosis and detection items comprise off-line oil gas analysis, partial discharge detection, direct current resistance detection, insulation resistance detection, high-frequency partial discharge detection, ultrasonic signal detection and ultrahigh frequency detection data input in winding voltage ratio detection and charged detection. The detection data of off-line oil gas analysis, partial discharge detection, high-frequency partial discharge detection, ultrasonic signal detection and ultrahigh frequency detection are unqualified, and the defect diagnosis result is discharge defect CF (confidence) =0.99; wherein, high energy discharge CF =0.4, low energy discharge CF =0.6, CF =0.8 without solid insulation and CF =0.8 in suspension discharge and giving the defect position as suspension discharge at the flange of the medium-pressure sleeve in the B phase, the corresponding defect mode is insulating oil defect, and the sub-defect mode is partial discharge in oil.
For the severity analysis formula, currently, a set of corresponding severity differentiation analysis formula is formed for each type of typical defect set of power transformation main equipment (such as a transformer, a breaker, a disconnecting switch, a grounding disconnecting link, a lightning arrester, a mutual inductor and the like). Taking the device type as a main transformer, the part to which the device belongs as a current carrying system, and the defect mode as a winding short circuit as an example, a calculation formula 1 is as follows:
equation 1:
key parameters: a direct current leakage current;
the data source is as follows: dc leakage test-leakage current (uA) of the winding;
the calculation method comprises the following steps: x = the same item in the measurement value/handover test table;
calculating the formula: calculation by four-region linear algorithm
Zone 1: x <1.0 severity: 0;
zone 2: x is more than or equal to 1.0 and less than 1.5 severity: ((X-1.0)/0.5) × 0.5;
zone 3: x is more than or equal to 1.5 and less than 3.0 severity: ((X-1.5)/1.5) × 0.5+0.5;
zone 4: x severity of 3.0 ≦ X: 1
And inputting the severity analysis result into a state evaluation and result output unit, giving recommended measures by the state evaluation and result output unit, carrying out tests according to the measures by operation and maintenance personnel, supplementing data to the defect diagnosis model, and diagnosing the defect diagnosis model again according to the newly supplemented data. The cases were the same as for the defect analysis, with a first diagnosis having a severity of 0.75; the severity of the second diagnosis is 1; the severity of the third diagnosis was 1.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, it is possible to make various improvements and modifications without departing from the technical principle of the present invention, and those improvements and modifications should be considered as the protection scope of the present invention.
Claims (4)
1. A method for diagnosing a substation main device based on a defect mode is characterized by comprising the following steps:
s1, constructing a defect diagnosis model:
s11, defining a defect mode, and defining typical defect types of the power transformation main equipment;
s12, defining detection items, and carrying out category grouping on all state information of the transformer main equipment under the existing conditions according to the source of the state information and the incidence relation between the state information and the defect mode to form the detection items for judging whether the detection is qualified or not;
s13, defining an analysis matrix, wherein the analysis matrix is a D multiplied by M matrix, D is a defect set, M is a detection item set, a defect probability exists in a defect mode reflected by each detection item, the defect probability value is a decimal between 0 and 1, the numerical value is a statistical result of empirical data and/or an empirical value provided by an expert, determining the corresponding relation between the detection item and the defect mode, and endowing each corresponding relation with a basic defect probability;
s14, defining a defect probability comprehensive algorithm for comprehensively calculating the defect probability of a plurality of unqualified detection items related to one defect;
s2, model application:
s21, judging detection items, namely inputting full-dimensional data into the detection items, judging whether the input data is abnormal or not through the detection items, and screening out unqualified detection items;
s22, matching the defect modes to obtain the defect mode corresponding to each unqualified detection item and the corresponding defect probability, and specifically comprising the following steps:
the detection items judged to be parameter-free detection items and qualified detection items with parameters in the rejection detection item judgment stage cannot participate in defect diagnosis of equipment, and invalid data dimensionality is actively reduced;
performing association retrieval on unqualified detection items with parameters as the detection item determination result by combining the analysis matrix to obtain a defect mode corresponding to each unqualified detection item and a corresponding defect probability;
s23, defect probability synthesis and severity analysis, which comprises the following parallel steps:
s231, defect probability synthesis, namely, comprehensively calculating the defect mode and the basic defect probability of the unqualified detection item corresponding to the defect mode, wherein the defect probability synthesis specifically comprises the following steps: traversing the analysis result of defect mode matching, carrying out comprehensive calculation on the defect mode and the basic defect probability of a plurality of unqualified detection items corresponding to a certain defect mode, and outputting the unique defect probability of each defect mode and the integral defect probability of the equipment;
s232, analyzing and calculating the severity, namely a quantitative index used for representing the defect/defect development degree in the model, and specifically comprising the following steps: comparing the test measured value with a standard value in the same item or related procedures in a handover test table, and mapping the ratio to a [0,1] interval;
and S24, outputting a diagnosis result based on the calculation result of the step S23.
2. The method for diagnosing a substation main equipment based on a defect mode according to claim 1, wherein the step S21 specifically includes: inputting full-dimensional data into detection items to form three types of detection item sets:
1) The method comprises the steps that a parameter-free detection item set is a detection item without an access model or a detection means;
2) A qualified test item set with parameters;
3) A set of disqualified test items with parameters.
3. A system for diagnosing a substation master based on a fault mode, the system comprising:
the model building module is used for building a defect model;
the model application module is used for carrying out defect diagnosis;
the model building module specifically comprises:
the defect mode defining unit is used for defining a defect mode and defining a typical defect type of the power transformation main equipment;
the detection item definition unit is used for defining detection items, and carrying out category grouping on all state information under the existing conditions of the power transformation main equipment according to the source of the state information and the incidence relation between the state information and the defect mode to form a detection item for judging whether the detection is qualified or not;
an analysis matrix definition unit, which defines an analysis matrix, wherein the analysis matrix is a D multiplied by M matrix, D is a defect set, M is a detection item set, a defect probability exists in a defect mode reflected by each detection item, the defect probability value is a decimal between 0 and 1, the numerical value is a statistical result of empirical data and/or an empirical value provided by an expert, the corresponding relation between the detection item and the defect mode is determined, and each corresponding relation is endowed with a basic defect probability;
the defect probability comprehensive algorithm definition unit is used for defining a defect probability comprehensive algorithm and comprehensively calculating the credibility of a plurality of unqualified detection items related to one defect;
the model application module specifically comprises:
the detection item judgment unit is used for inputting the full-dimensional data into a detection item, judging whether the input data is abnormal or not through the detection item, and screening out unqualified detection items;
a defect pattern matching unit, configured to obtain a defect pattern corresponding to each unqualified inspection item and a corresponding defect probability, where the defect pattern matching unit is specifically configured to:
the detection items judged to be parameter-free detection items and qualified detection items with parameters in the rejection detection item judgment stage cannot participate in the analysis of equipment defects, and invalid data dimensions are actively reduced;
performing association retrieval on unqualified detection items with parameter judgment results in the detection item judgment stage by combining the analysis matrix to obtain a defect mode corresponding to each unqualified detection item and corresponding defect probability;
the defect probability synthesis and severity analysis unit comprises: the defect probability comprehensive subunit is used for comprehensively calculating the defect mode and the basic defect probability of the unqualified detection item corresponding to the defect mode; the severity analysis subunit is used for analyzing and calculating the severity, namely a quantitative index used for representing the defect/defect development degree in the model; the defect probability synthesis subunit is specifically configured to: traversing the analysis result of defect mode matching, carrying out comprehensive calculation on the defect mode and the basic defect probability of a plurality of unqualified detection items corresponding to a certain defect mode, and outputting the unique defect probability of each defect mode and the integral defect probability of the equipment; the severity analysis subunit is specifically configured to: comparing the test measured value with a standard value in the same item or related procedures in a handover test table, and mapping the ratio to a [0,1] interval;
and the diagnosis result output unit is used for outputting the calculation result based on the defect probability integration and severity analysis unit.
4. The system according to claim 3, wherein the detection item determination unit is specifically configured to: inputting full-dimensional data into detection items to form three types of detection item sets:
1) The method comprises the steps that a parameter-free detection item set is a detection item without an access model or a detection means;
2) A qualified test item set with parameters;
3) And (4) collecting unqualified detection items with parameters.
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