CN113484646A - Transformer substation secondary circuit complex fault positioning device and diagnosis method - Google Patents
Transformer substation secondary circuit complex fault positioning device and diagnosis method Download PDFInfo
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Abstract
The invention discloses a transformer substation secondary circuit complex fault positioning device and a diagnosis method, wherein the fault positioning device comprises a main control CPU, a measurement unit module and a data communication preposed unit; the main control CPU communicates data information with the data communication preposition unit; the function of the measurement unit module is the collection function of various information quantities of events, the function of the data communication preposition unit is to complete data communication with each measurement unit, and is responsible for temporary storage of data information, receiving the command of the device host control system and issuing the command to each measurement unit, completing time synchronization and clock correction of each measurement unit, and sending data to the host control system. And the fault positioning device acquires data information and performs logical diagnosis reasoning on different direct current determined data information acquisition points. The diagnosis method adopts a mathematical algorithm of pattern matching to carry out analysis, diagnosis and reasoning, and provides comprehensive data information for accurate reasoning and positioning of faults, thereby ensuring the accuracy and effectiveness of the faults.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a transformer substation secondary circuit complex fault positioning device and a diagnosis method.
Background
Some secondary circuits of the current transformer substation have complex formation mechanisms (typical complex faults include grounding of a direct current system of the transformer substation and series faults of an alternating current and direct current circuit), and the obvious characteristic of the complex formation mechanisms is uncertainty of individual characteristics presented by the faults, which is different from other faults. Besides some fault inherent characteristics which are clear, the method also has more individual unique characteristics which present uncertainty. The concrete aspects are as follows:
for a direct current ground fault, different fault points present more different fault characteristic information besides causing the voltage to ground of the substation direct current system to offset. For example, the dc ground faults of different branches have different ground resistances and different offset degrees of the dc bus voltage. And when the ground fault occurs to different branches, the individual characteristics of the branches show uncertainty. Namely, the grounding resistance and the direct current offset voltage of the grounding fault of different branches show the same fault characteristics. Meanwhile, the direct current grounding characteristics of the same branch are different under different events and different environmental conditions. For the direct-current grounding fault, the existing direct-current grounding branch line selection device carries out branch line confirmation according to the current balance principle of a direct-current branch line, the principle is simple and old, the detection sensitivity is low, the wrong selection, wrong selection and missing selection rates of the grounding fault branch line are high, the specific fault position of the grounding branch line cannot be determined, and therefore the auxiliary support effect of effective fault processing cannot be really achieved.
Secondly, for the series fault of the alternating current and direct current system, the individuality of the frequent fault characteristics is strong. Some faults present the characteristic phenomena of grounding and instantaneous recovery of a direct current system. Some of the monitoring systems present a large amount of irrelevant alarm information frequently sent by mistake by the instant monitoring system, fault characteristics of instant resetting and the like. At present, the fault can be judged only according to experience, and a reliable auxiliary supporting tool is not available.
Disclosure of Invention
The invention aims to solve the technical problem of providing a transformer substation secondary circuit complex fault positioning device and a diagnosis method, which adopt a mode matching mathematical algorithm to carry out analysis diagnosis and reasoning and provide comprehensive data information for accurate reasoning and positioning of faults so as to ensure the accuracy and effectiveness of the faults.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
a transformer substation secondary circuit complex fault positioning device comprises a main control CPU, a measurement unit module and a data communication preposition unit;
the main control CPU communicates data information with the data communication preposition unit;
the function of the measurement unit module is the collection function of various information quantities of events, the function of the data communication preposition unit is to complete data communication with each measurement unit, and is responsible for temporary storage of data information, receiving the command of the device host control system and issuing the command to each measurement unit, completing time synchronization and clock correction of each measurement unit, and sending data to the host control system.
As a further improvement of the positioning device, the measuring unit module comprises a clamp type current measuring component, a contact type voltage and current measuring component and a temperature/humidity non-electrical quantity measuring component;
the fault occurrence of a secondary circuit is regarded as an event occurrence, and the change of the electrical quantity information and the change of the non-electrical quantity information which are accompanied by the fault occurrence of the secondary circuit of the measured detection point form the characteristic variable of the event;
the clamp type current measuring assembly, the contact type voltage and current measuring assembly and the temperature/humidity non-electrical quantity measuring assembly finish the real-time collection of the change of the characteristic variable, record data information and time information for generating the data, and send the data to the device main control system.
As a further improvement of the positioning device, the main control CPU performs information interaction with the transformer substation monitoring system, receives command detection information sent by the transformer substation monitoring system, simultaneously sends a command reasoning detection result to the transformer substation monitoring system for transformer substation operation and maintenance personnel to review, and receives a fault data updating command sent by the monitoring system to update the database of the monitoring system.
As a further improvement of the positioning device, the fault data refers to a certain fault event which is not identified by manual work, and after receiving the instruction of the monitoring system, the main control CPU records and accumulates the data measurement information corresponding to the fault event data to form a new sample of the type of event.
As a further improvement of the positioning device, the main control CPU receives a clock message issued by the monitoring system, and completes the clock correction of the host, the data communication front-end unit and the measurement unit module.
A diagnosis method using a transformer substation secondary circuit complex fault positioning device firstly determines the fact that: if the complex fault occurring at a certain determined position of a certain direct current branch is expressed as an event, the occurring event is called a sample; for a plurality of samples of the event, some data information presents a physical characteristic of normal distribution, namely information which is completely consistent with the data information cannot be achieved by means of a group of determined data, but because the data information presents a certain distribution characteristic on a macroscopic level, the data which conforms to the characteristic has the possibility of being a sample of the event to a certain extent, and the higher the matching degree is, the higher the probability of conforming to the event is; the complex fault of the secondary circuit is like the moving track of electrons in atoms, under the macroscopic condition, the moving track of the secondary circuit is represented as different electron clouds, and the probability distribution characteristics of the moving track of the secondary circuit are reflected by the shapes of the different electron clouds.
As a further improvement of the diagnosis method, based on the fact, the logical inference method of pattern matching of similarity recognition is as follows: for some event P, it contains the following feature vectors:
P＝[p′1,p′2,......,p′n]；
each vector contains the following variables:
p′i＝(fi1,fi2,......,fiq)；
then:
distribution characteristics of independent event random variables:
according to the statistical theory, for an event P, the events which occur are independent of each other, the variable values of the events are subject to the characteristic rule of normal distribution, and for n event sequence sets of the independent events P of the same type: { P1,P2,......,PnEach event PiVariable f in (1)i,jWherein { i belongs to (0-n), j belongs to (0-q) } obeys the characteristic of normal distribution; i.e. for the variable fi,jThe values obey the distribution characteristics of a normal probability density function:
for a set of events P: { P1,P2,......,PnAny one of independent variables f ini,jWill become a sample space
The mean μ and variance σ of the variable can be calculated;
thus, for each variable f of the event PijAll will obtain an average value
A standard pattern of events P is thus formed:
as a further improvement of the diagnostic method, a weighted model of the event features: some of the variables of the event P are changed, and have a larger value; but the change of the variable values is small and stable; the same event, the larger change of the value and the higher distribution probability indicate that the value is higher in activity degree; similarly, a smaller change indicates a lower activity level for the value;
corresponding to the event P, the mean square error matrix of the sample is:
the mean square error of the sample reflects the degree of deviation of each variable of the event from the standard mean value of the variable;
defining a variable weight coefficient S:
s (x) σ · f (x), where σ is the sample mean square error of the variable, and f (x) is its probability distribution density function;
the probability distribution density function f (x) reflects the magnitude of probability value corresponding to the value of a variable with normal distribution characteristics under specific mean and variance, and the physical meaning of the product of the two indicates that: for any sample, the function s (x) reflects a quantitative index of the matching degree of the variables related to the specific event after the values of the variables refer to the probability distribution characteristics and the standard pattern under the condition that the standard pattern is taken as a reference.
As a further improvement of the diagnostic method, the pattern matching method is as follows:
for a specific event P' which occurs, judging whether the event P and the event P belong to the same type of event, and adopting the following modes:
s (X) ═ P' (X) · P (σ), that is:
using S (X) and standard PbzThe absolute difference of the corresponding variable is reflected by the difference degree between the standard value and the corresponding variable, and the matrix M is used forcz(X) is as follows:
in an ideal, perfect match situation, if a particular event P' is exactly one sample of event P, then MczThe value of each variable in (X) should be 0, however, in practice there must be a deviation, there is a certain amount of deviation, and it cannot be denied that event P' is not a sample of event P.
It is also necessary to consider the other case, namely the case where said event is for a specific event tkThe temporal events, but the real objective physical events, an event is generated and changed with the time, and different times correspond to different events P, i.e. a set of event sequences is formed:
Pt jh＝{Pt1,Pt2,...,Ptk....,Ptmthe pattern matching of events should not be limited to matching of variable values at specific times only, but also the development of events, i.e. the degree of matching at different times,
Pt jhwill generate different corresponding different time
Thereby forming a set of sequences of difference values:
set Mcz|T(X) each element in (X) is formed by the development of the same event over time; the event P development is continuous, so that the value of the event P development is continuously related; similarly, there is a correlation between the values of the events P 'at different times, and if the event P' is a sample event formed by the events P, the set M formed is in particularcz|TAnd (X) the corresponding variables contained in each element have linear correlation, if the correlation degree is higher, the P' is determined to be a sample of the event P and the event P is the same as the event P, otherwise, the event P cannot be considered to belong to the same event.
As a further improvement of the diagnostic method, the concept of correlation coefficient of statistical theory is utilized to detect the degree of correlation between the variables, and the analysis method is as follows:
for a corresponding set of difference values M with M time instantscz|T(X) two successive instants t of aggregation of the elementsiAnd ti+1Is an element of
And if the sample data size of the whole set is m, the correlation coefficient rho calculation formula is as follows:
whereby an independent variable f for an event PijWill generate an m correlation coefficients piThe data series of (2) is subjected to summation operation to obtain a data variable of a correlation coefficient:
Each variable f corresponding to an event PijEventually, a matrix of correlation coefficients is formed:
the data correlation of the outstanding variable, its correlation coefficient is close to 1, and less than 0.5, consider its degree of correlation to be lower;
when close to 0, it can be considered that there is no correlation; therefore, for data with a lower degree of similarity, it is weakened; taking the above factors into consideration, for the set ρΣPerforming mathematical operation, each variable operation formula
Calculating to obtain a new correlation coefficient matrix rho'ΣIt is a q × n matrix;
for matrix ρ'ΣThe values of the elements in (a) are summed:
the condition that the correlation coefficient is greater than 0.6 can be considered to have correlation is changed into 0 quantity 6 after mathematical square operation, so when the correlation coefficient is greater than 0.6
When the condition is satisfied, it can be inferred that the event P' is a sample event of the event P.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
the invention provides a method and a device for diagnosing and reasoning the complex fault. And determining data information acquisition points for different direct currents. The collection point information contains electric quantity information such as current, voltage and the like, and also comprises non-electric quantity information such as temperature, humidity and the like. The mathematical algorithm matched with the mode is adopted for analysis, diagnosis and reasoning, and comprehensive data information is provided for accurate reasoning and positioning of the fault, so that the accuracy and the effectiveness of the fault are ensured.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic view of a measurement cell module;
FIG. 2 is a schematic diagram of a data communication head unit of the apparatus;
FIG. 3 is a schematic diagram of the device host hardware mechanism;
FIG. 4 is a host CPU control system software flow diagram;
fig. 5 is a variable diagram of a branch circuit.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
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 relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting.
As shown in the figures 1-3 of the drawings,
a transformer substation secondary circuit complex fault positioning device comprises a main control CPU, a measurement unit module and a data communication preposition unit;
the main function of the measuring unit module is the acquisition function of various information quantities of events. A failure of a secondary loop may be considered an event. The occurrence of this event is accompanied by a change in the electrical quantity information (including current and voltage information) and a change in the non-electrical quantity information (including a change in the quantity of information such as temperature and humidity) at the monitoring point set manually. These quantities of information will be part of the characteristic variables of the event. The measuring unit module collects the change of the information quantity in real time, records data information, time information for generating the data and other data, and generates the data to the device main control system. The data communication preposition unit mainly completes data communication with each measurement unit, is responsible for temporarily storing data information, receives a command of a device host control system and issues the command to each measurement unit, completes time synchronization and clock correction of each measurement unit, sends data to the host control system and the like.
The device host system communicates data information with the data communication front-end unit. Data information and command information are transmitted. Meanwhile, the host CPU system and the transformer substation monitoring system perform information interaction, mainly receive command detection information sent by the transformer substation monitoring system, and simultaneously send a command reasoning detection result to the transformer substation monitoring system for transformer substation operation and maintenance personnel to review. And receiving a fault data updating command issued to the monitoring system to update the database of the monitoring system. The fault data is a certain fault event which is not manually identified, and after the host receives the instruction of the monitoring system, the data measurement information record corresponding to the fault event data is accumulated to become a new sample of the type of event. The device host provides a human-computer interaction interface to complete the functions of program debugging, information retrieval and the like. The host system receives the clock message issued by the monitoring system, and completes the clock correction of the host, the data preposition unit and the measurement unit.
The software flow of the host CPU control system is shown in FIG. 4, and the software system comprises a main program and an interrupt program. The main program completes clock correction, receiving of command information of the monitoring system and data interaction. The interrupt program mainly completes the diagnosis and reasoning function of the fault.
The invention provides a method and a device for diagnosing and reasoning the complex fault. Firstly, data information acquisition points are determined for different direct currents. The collection point information contains electric quantity information such as current, voltage and the like, and also comprises non-electric quantity information such as temperature, humidity and the like. The accurate reasoning and positioning of the fault provides comprehensive data information, thereby ensuring the accuracy and effectiveness of the fault. The main idea of reasoning is to use a mathematical algorithm of pattern matching to perform analysis, diagnosis and reasoning.
The pattern matching algorithm is based on the individuality that the data information generated by a fault event reflects the fault characteristics of the fault event belonging to the fault event. Individuality here refers to the characteristics of affiliation to the present fault event. The system refers to the fault characteristics of a certain specific position of a certain branch circuit.
The patent firstly determines the fact that the secondary loop complex fault is: if a complex fault occurring at a certain determined position of a certain direct current branch is expressed as an event, the occurring event is called a sample. And the value of the related data information caused by a plurality of such events is not completely determined. That is, an event is matched against a value having completely certain information, and the event of the type cannot be matched. However, for multiple samples of the event, some data information exhibits a normally distributed physical property. That is, information completely consistent with a certain set of data cannot be obtained by means of the certain set of data, but because the data information of the certain set of data shows a certain distribution characteristic on a macroscopic level, data conforming to the characteristic has a possibility of being a sample of the event to some extent, and the higher the matching degree is, the higher the probability of conforming to the event is. The secondary loop complex fault is like the trajectory of an electron in an atom. For a single electron, even if its motion parameters are known, it is not possible to determine where it will be at a particular time interval thereafter. But under macroscopic conditions, the trajectory of its motion appears as a distinct cloud of electrons. The different electron cloud shapes reflect the probability distribution characteristics of their motion trajectories.
Based on the facts, the main idea of pattern matching is to determine the related parameter information of the normal distribution characteristic of a certain event by using the determined sample data as accumulation after the certain event has occurred by means of a certain mathematical reasoning algorithm. For a currently occurring event, matching already existing events according to a mathematical reasoning algorithm, thereby deriving a reasoning for the event.
The similarity recognition pattern matching logic reasoning method comprises the following steps:
for some event P, it contains the following feature vectors:
P＝[p′1,p′2,......,p′n]；
each vector contains the following variables:
p′i＝(fi1,fi2,......,fiq)；
then:
distribution characteristics of independent event random variables:
according to the statistical theory, the probability of the occurrence of mutually independent random events in the natural physical world follows the regular characteristic of normal distribution. Based on this theory, for event P, the events that occur are independent of each other. The variable values contained in the normal distribution model obey the characteristic rule of normal distribution. Event sequence sets for n independent events P of the same type are noted: { P1,P2,......,PnEach event PiVariable f in (1)i,jWhere { i ∈ (0. about.n), j ∈ (0. about.q) } should obey the characteristics of a normal distribution. I.e. for the variable fi,jThe values obey the distribution characteristics of a normal probability density function:
For a set of events P: { P1,P2,......,PnAny one of independent variables f ini,jWill become a sample space
The mean μ and variance σ of the variables can be calculated.
Thus, for each variable f of the event PijAll will obtain an average value
A standard pattern of events P is thus formed:
weight model of event features: of the variables of the event P, some variables are changed, and there is a change in a larger number. But the change in the value of some variables is small and stable. The same event, with a larger value change and a higher probability of distribution, indicates that the value is more active. Similarly, a smaller change indicates a lower activity level for the value.
Corresponding to the event P, the mean square error matrix of the sample is:
description of the drawings: the mean square error of the sample reflects the degree of deviation of each variable of the event from its standard mean.
Defining a variable weight coefficient S:
s (x) σ · f (x), where σ is the sample mean square error of the variable and f (x) is its probability distribution density function.
Description of the drawings: the probability distribution density function f (x) reflects the magnitude of probability value corresponding to the value of a variable with normal distribution characteristics under specific mean and variance, and the physical meaning of the product of the two indicates that: for any sample, the function s (x) reflects a quantitative index of the matching degree of the variables related to the specific event after the values of the variables refer to the probability distribution characteristics and the standard pattern under the condition that the standard pattern is taken as a reference.
Brief description of the pattern matching method:
for a specific event P' which occurs, judging whether the event P and the event P belong to the same type of event, and adopting the following modes:
s (X) ═ P' (X) · P (σ), that is:
using S (X) and standard PbzThe absolute difference of the corresponding variable is reflected by the difference degree between the standard value and the corresponding variable, and the matrix M is used forcz(X) is as follows:
in an ideal, perfect match situation, if a particular event P' is exactly one sample of event P, then MczThe value of each variable in (X) should be 0. However, the actual situation is certainly biased. There is a certain amount of deviation that cannot be negated that event P' is not a sample of event P.
It is also necessary to consider the other case, namely the case where said event is for a specific event tkThe event of time. However, in real objective physical events, the occurrence of an event changes with the time, and different times correspond to different events P, i.e. a set of event sequences is formed:
Pt jh＝{Pt1,Pt2,...,Ptk....,Ptm}. The pattern matching of events should not be limited to matching of variable values at a particular time, but should also take into account the development of the event, i.e. the degree of matching at different times.
Pt jhWill generate different corresponding different time
Thereby forming a set of sequences of difference values:
obviously, set Mcz|TEach element in (X) is formed by the development of the same event over time. The event P development is continuous, and therefore, the value is continuously correlated. Similarly, there is a correlation between the values of the event P' at different times. If the event P' is a sample event formed by the event P, the set M formed in particularcz|TThere must be a linear correlation between the corresponding variables contained in each element of (X). If the degree of correlation is high, then,it can be determined that P' is a sample of event P and that event P is homogeneous. Otherwise, it cannot be considered as belonging to the same event.
The degree of correlation between its variables can be detected using the concept of correlation coefficients of statistical theory. The analysis method is as follows: for a corresponding set of difference values M with M time instantscz|T(X) two successive instants t of aggregation of the elementsiAnd ti+1Is an element of
And
and if the sample data size of the whole set is m, the correlation coefficient rho calculation formula is as follows:
whereby an independent variable f for an event PijWill generate an m correlation coefficients piThe data series of (1). And carrying out summation operation on the data to obtain a data variable of a correlation coefficient:
Each variable f corresponding to an event PijEventually, a matrix of correlation coefficients is formed:
the degree of correlation is considered to be low when the correlation coefficient of a variable whose data correlation is prominent is close to 1 and less than 0.5. When close to 0, it can be considered that there is no correlation. Thus, for numbers with a lower degree of similarityAccordingly, it can be weakened. Taking the above factors into consideration, for the set ρΣPerforming mathematical operation, each variable operation formula
Calculating to obtain a new correlation coefficient matrix rho'ΣIt is a q × n matrix.
For matrix ρ'ΣThe values of the elements in (a) are summed:
the condition that the correlation coefficient is greater than 0.6 can be considered to have correlation is changed into 0 quantity 6 after mathematical square operation, so when the correlation coefficient is greater than 0.6
When the condition is satisfied, it can be inferred that the event P' is a sample event of the event P.
The foregoing reasoning process for dc ground fault at one site of only one branch of the event P. In fact, there are various branches in the substation, including control, protection, remote signaling, wave recording, etc. For a ground fault occurring at a certain position of a first group of control loops of a certain 220kV circuit breaker, the formed sample can be transplanted to a ground fault occurring at a certain position of a first group of control loops of another 220kV circuit breaker, thereby forming a sample of a ground fault occurring at a certain position of another branch circuit. Thus, event P is given an attribute M, M ═ M that characterizes its legs1,m2,......,m3]. Other events with the same characteristic variables can be migrated to each other. Of course, the event P contains a variable that is the variable to which the rate belongs. After the event P is transplanted, the variables are also associated correspondingly and transformed into the variables of the transplanted event, which are called mapping of the event variables. Mapping description as shown in fig. 5, mapping transformation exists between the relevant variables to which the event P and the event Q belong:
the analysis is an intelligent reasoning method for a reasoning process of pattern matching of a secondary circuit complex secondary circuit fault event.
Claims (10)
1. The utility model provides a complicated fault positioning device of transformer substation's secondary circuit which characterized in that: the system comprises a main control CPU, a measuring unit module and a data communication preposition unit;
the main control CPU communicates data information with the data communication preposition unit;
the function of the measurement unit module is the collection function of various information quantities of events, the function of the data communication preposition unit is to complete data communication with each measurement unit, and is responsible for temporary storage of data information, receiving the command of the device host control system and issuing the command to each measurement unit, completing time synchronization and clock correction of each measurement unit, and sending data to the host control system.
2. The complex fault location device of the substation secondary circuit of claim 1, characterized in that: the measuring unit module comprises a clamp type current measuring assembly, a contact type voltage and current measuring assembly and a temperature/humidity non-electrical quantity measuring assembly; the fault occurrence of a secondary circuit is regarded as an event occurrence, and the change of the electrical quantity information and the change of the non-electrical quantity information which are accompanied by the fault occurrence of the secondary circuit of the measured detection point form the characteristic variable of the event;
the clamp type current measuring assembly, the contact type voltage and current measuring assembly and the temperature/humidity non-electrical quantity measuring assembly finish the real-time collection of the change of the characteristic variable, record data information and time information for generating the data, and send the data to the device main control system.
3. The complex fault location device of the substation secondary circuit according to claim 2, characterized in that: the main control CPU performs information interaction with the transformer substation monitoring system, receives command detection information sent by the transformer substation monitoring system, simultaneously sends a command reasoning detection result to the transformer substation monitoring system for transformer substation operation and maintenance personnel to review, and receives a fault data updating command sent by the monitoring system to update a database of the main control CPU.
4. The complex fault location device of the substation secondary circuit of claim 3, characterized in that: the fault data is a certain fault event which is not manually identified, and after the main control CPU receives the instruction of the monitoring system, the data measurement information corresponding to the fault event data is recorded and accumulated to form a new sample of the type of event.
5. The complex fault location device of the substation secondary circuit of claim 4, characterized in that: and the main control CPU receives a clock message issued by the monitoring system, and completes the clock correction of the host, the data communication preposition unit and the measurement unit module.
6. A diagnosis method using a transformer substation secondary circuit complex fault positioning device is characterized in that: such a fact is first determined for a complex fault of the secondary loop: if the complex fault occurring at a certain determined position of a certain direct current branch is expressed as an event, the occurring event is called a sample; for a plurality of samples of the event, some data information presents a physical characteristic of normal distribution, namely information which is completely consistent with the data information cannot be achieved by means of a group of determined data, but because the data information presents a certain distribution characteristic on a macroscopic level, the data which conforms to the characteristic has the possibility of being a sample of the event to a certain extent, and the higher the matching degree is, the higher the probability of conforming to the event is;
the complex fault of the secondary circuit is like the moving track of electrons in atoms, the moving characteristic of a single electron at a certain moment under the microscopic condition cannot be determined, the moving track of the single electron is represented as different electron clouds under the macroscopic condition, and the probability distribution characteristics of the moving track of the single electron cloud are reflected by the shapes of the different electron clouds.
7. The diagnosis method using the complex fault location device of the substation secondary circuit according to claim 6, characterized in that: based on the facts, the logical inference method of pattern matching of similarity recognition is as follows: for some event P, it contains the following feature vectors:
P＝[p′1,p′2,......,p′n]；
each vector contains the following variables:
p′i＝(fi1,fi2,......,fiq)；
then:
distribution characteristics of independent event random variables:
according to the statistical theory, for an event P, the events which occur are independent of each other, the variable values of the events are subject to the characteristic rule of normal distribution, and for n event sequence sets of the independent events P of the same type: { P1,P2,......,PnEach event PiVariable f in (1)i,jWherein { i belongs to (0-n), j belongs to (0-q) } obeys the characteristic of normal distribution; i.e. for the variable fi,jThe values obey the distribution characteristics of a normal probability density function:
for a set of events P: { P1,P2,......,PnAny one of independent variables f ini,jWill become a sample space
The mean μ and variance σ of the variable can be calculated;
thus, for each variable f of the event PijAll will obtain an average value
A standard pattern of events P is thus formed:
8. the diagnosis method using the complex fault location device of the substation secondary circuit according to claim 7, characterized in that: weight model of event features: some of the variables of the event P are changed, and have a larger value; but the change of the variable values is small and stable; the same event, the larger change of the value and the higher distribution probability indicate that the value is higher in activity degree; similarly, a smaller change indicates a lower activity level for the value;
corresponding to the event P, the mean square error matrix of the sample is:
the mean square error of the sample reflects the degree of deviation of each variable of the event from the standard mean value of the variable;
defining a variable weight coefficient S:
s (x) σ · f (x), where σ is the sample mean square error of the variable, and f (x) is its probability distribution density function;
the probability distribution density function f (x) reflects the magnitude of probability value corresponding to the value of a variable with normal distribution characteristics under specific mean and variance, and the physical meaning of the product of the two indicates that: for any sample, the function s (x) reflects a quantitative index of the matching degree of the variables related to the specific event after the values of the variables refer to the probability distribution characteristics and the standard pattern under the condition that the standard pattern is taken as a reference.
9. The diagnosis method using the complex fault location device of the substation secondary circuit according to claim 8, characterized in that: the pattern matching method is as follows:
for a specific event P' which occurs, judging whether the event P and the event P belong to the same type of event, and adopting the following modes: s (X) ═ P' (X) · P (σ), that is:
using S (X) and standard PbzThe absolute difference of the corresponding variable is reflected by the difference degree between the standard value and the corresponding variable, and the matrix M is used forcz(X) is as follows:
in an ideal, perfect match situation, if a particular event P' is exactly one sample of event P, then Mcz(X) the value of each variable should be 0, however, in practice there must be a deviation, there is a certain amount of deviation, it cannot be denied that event P' is not a sample of event P;
it is also necessary to consider the other case, namely the case where said event is for a specific event tkThe temporal events, but the real objective physical events, an event is generated and changed with the time, and different times correspond to different events P, i.e. a set of event sequences is formed:
Pt jh＝{Pt1,Pt2,...,Ptk....,Ptmthe pattern matching of events should not be limited to matching of variable values at specific times only, but also the development of events, i.e. the degree of matching at different times,
Pt jhwill generate different corresponding different time
Thereby forming a set of sequences of difference values:
set Mcz|T(X) each element in (X) is formed by the development of the same event over time; the event P development is continuous, so that the value of the event P development is continuously related; similarly, there is a correlation between the values of the events P 'at different times, and if the event P' is a sample event formed by the events P, the set M formed is in particularcz|TAnd (X) the corresponding variables contained in each element have linear correlation, if the correlation degree is higher, the P' is determined to be a sample of the event P and the event P is the same as the event P, otherwise, the event P cannot be considered to belong to the same event.
10. The diagnosis method using the complex fault location device of the substation secondary circuit according to claim 9, characterized in that: the concept of a correlation coefficient of a statistical theory is utilized to detect the degree of correlation between variables, and the analysis method is as follows:
for a corresponding set of difference values M with M time instantscz|T(X) two successive instants t of aggregation of the elementsiAnd ti+1Is an element of
And
and if the sample data size of the whole set is m, the correlation coefficient rho calculation formula is as follows:
whereby an independent variable f for an event PijWill generate an m correlation coefficients piThe data series of (2) is subjected to summation operation to obtain a data variable of a correlation coefficient:
Each variable f corresponding to an event PijEventually, a matrix of correlation coefficients is formed:
the data correlation of the outstanding variable, its correlation coefficient is close to 1, and less than 0.5, consider its degree of correlation to be lower; when close to 0, it can be considered that there is no correlation; therefore, for data with a lower degree of similarity, it is weakened; taking the above factors into consideration, for the set ρ∑Performing mathematical operation, each variable operation formula
Calculating to obtain a new correlation coefficient matrix rho'∑It is a q × n matrix;
for matrix ρ'∑The values of the elements in (a) are summed:
the condition that the correlation coefficient is greater than 0.6 can be considered to have correlation is changed into 0 quantity 6 after mathematical square operation, so when the correlation coefficient is greater than 0.6
When the condition is satisfied, it can be inferred that the event P' is a sample event of the event P.
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