CN111931423A - Power transformer fault diagnosis method based on fuzzy proximity of state data - Google Patents
Power transformer fault diagnosis method based on fuzzy proximity of state data Download PDFInfo
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Abstract
A power transformer fault diagnosis method based on state data fuzzy proximity belongs to the technical field of power transformer state maintenance, difference between a transformer and a fault transformer in operation state evaluation indexes and time sequence is measured by establishing a fuzzy proximity operator, fault diagnosis is carried out according to the difference, and the purpose of diagnosing the power transformer fault is further achieved. The diagnosis behavior of the invention can change along with the change of the historical data of the actual maintenance work, the change of the historical data can directly cause the change of the diagnosis behavior, the invention is very important for dynamically adapting to the new fault, and the invention has better application value in the actual electric power maintenance process.
Description
Technical Field
The invention belongs to the technical field of power transformer state maintenance, and particularly relates to a power transformer fault diagnosis method based on state data fuzzy proximity.
Background
The state evaluation of the power transformer is the basis of the state maintenance of the power system, and the discovery of possible faults of the power transformer is very important for the safe and effective operation of a power grid.
In the field of power transformer fault diagnosis, the main methods adopted at present comprise: firstly, under the conditions that obvious faults occur to a power transformer and a power customer reports for repair, relevant equipment is directly disassembled, and the faults in the power transformer are found in a manual mode; secondly, carrying out nondestructive measurement on data and establishing an evaluation index model, wherein the method is characterized by being nondestructive and finding faults before the power transformer causes large loss, however, as the model is usually established in advance in a laboratory, the materials, the processing technology and the environment of the power transformer used in the production process are continuously changed, and many problems occur after the transformer is continuously used for a long time and the laboratory is difficult to completely reproduce the situations, the corresponding index model is possibly inaccurate in practical application and often has the problems of missing report and false report.
Therefore, there is a need in the art to provide a new method that can consider the characteristics of a power transformer changing over time in a specific model and a specific application environment, and further can effectively diagnose the fault of the power transformer.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method comprises the steps of measuring the difference between a transformer and a fault transformer in an operation state evaluation index and a time sequence by establishing a fuzzy proximity operator, and diagnosing the fault according to the difference so as to achieve the purpose of diagnosing the fault of the power transformer.
A power transformer fault diagnosis method based on state data fuzzy proximity is characterized by comprising the following steps: comprises the following steps which are sequentially carried out,
step one, inputting a historical data table History of a power transformer into a system, wherein the historical data table History comprises data recording time HDATA, an insulation resistance value HJY, an absorption ratio HXSB, a polarization index HJHZS, a winding direct-current resistance unbalance rate HBPHL of capacitance and dielectric loss, dielectric loss HJDSH and a fault state HGZZT, dividing the fault state HGZZT into four lists according to the level of the fault state HGZZT, and adding a weight field HSCQZ in each list;
step two, establishing a sequence weight calculation operator SCQZOpersor, wherein the input of the operator is a data list variable AList to be calculated, and calculating and modifying a sequence weight field HSCQZ of the data list AList through the operator;
step three, establishing a fuzzy adjacent operator fuzzy operator, wherein the operator inputs a power transformer state structure body ESTrcuct and a list BList to be calculated, and the output result is a fuzzy difference variable fuzzy result;
step four, processing the four lists obtained in the step one and classified according to the HGZZT grade of the fault state by using a sequence weight calculation operator SCQZOpersor;
and fifthly, inputting information of one transformer to construct an information structure BStrect, and performing fault diagnosis by using the fuzzy neighbor operator constructed in the third step and the four fault grade lists after combing in the fourth step.
The specific steps of the first step of dividing into four lists according to the grade of the fault state HGZZT are,
s101, inputting a historical data table History of the power transformer, wherein the History comprises the following fields: data recording time HDATA, insulation resistance value HJY, absorption ratio HXSB, polarization index HJHZS, winding direct-current resistance unbalance rate HBPHL of capacitance and dielectric loss, dielectric loss HJDSH and fault state HGZZT;
s102, selecting all data with a fault state HGZZT equal to a normal state in a History data table History, and storing the data in History1 in an order from small to large according to the value of HDATA;
s103, selecting all data with HGZZT equal to 'attention state' in the History data table History, and storing the data in History2 in sequence from small to large according to the value of HDATA;
s104, selecting all data with HGZZT equal to 'abnormal state' in the History data table History, and storing the data in History3 in sequence from small to large according to the value of HDATA;
s105, selecting all data with the HGZZT being equal to the 'serious state' in the History data table History, and storing the data in History4 in a sequence from small to large according to the value of HDATA;
s106, adding sequential weight fields HSCQZ for History1, History2, History3 and History4 respectively, the default content of the weight field HSCQZ being 0.
The specific operation steps of the second step are as follows,
s201, establishing a sequential weight calculation operator SCQZOpersor, wherein the input of the operator is a data list Alist;
s202, sequentially setting a counter SCQZCounter of the weight calculation operator to 1, setting an AList list entry variable SCQZNum to the number of entries of the list AList, and setting the previous dielectric loss PrevJD to AList [1]. hjhjzs, where hjzs is a polarization index and AList [1] is a first element of a list variable of data to be calculated;
s203, sequentially storing a variable SCQZTemp1 ═ tanh (SCQZCounter/SCQZNum) in the weight calculation operator, where tanh is a hyperbolic tangent function, SCQZCounter is the weight calculation operator, and SCQZNum is the number of list entries;
s204, sequentially calculating a temporary storage variable of the weight calculation operator, i.e. two SCQZTemp2 ═ ABS (AList [ scqzoperspective ]. hjhjhzs-PrevJD), where ABS is an absolute value, hjhjzs is a polarization index, PrevJD is a last dielectric loss, AList [ scqzoperspective ] is the scqzoperspective element of the to-be-calculated data list variable;
s205, when the weight calculation operator SCQZCounter is larger than the list entry variable, the calculation process is finished.
The specific operation steps of the third step are as follows,
s301, establishing a fuzzy neighbor operator fuzzy operator, wherein the operator inputs an electric transformer state structure body ESTrcuct and a list BList to be calculated, and the field structure of the ESTrcuct is the same as that of a historical data table History;
s302, the counter FuzzyCounter of the fuzzy neighbor operator is 1, the fuzzy neighbor operator entry variable FuzzyNum is the number of entries in the list BList, and the fuzzy accumulation variable FuzzySum is 0;
s303, acquiring a fuzzy temporary storage variable one FuzzyTemp1, a fuzzy temporary storage variable two FuzzyTemp2, a fuzzy temporary storage variable three FuzzyTemp3 and a fuzzy temporary storage variable four FuzzyTemp 4;
s304, acquiring a fuzzy temporary storage variable five FuzzyTemp5 ═ FuzzyTemp1+ FuzzyTemp2+ FuzzyTemp3+ FuzzyTemp 4;
s305, the counter uzzyCounter running to the F fuzzy neighbor is greater than the fuzzy neighbor entry variable FuzzyNum, the fuzzy difference variable FuzzyResult ═ FuzzySum, and the fuzzy difference variable FuzzyResult value is output.
The concrete operation steps of the step five are as follows,
s501, constructing an information structure BStruct, where the structure of BStruct is the same as a field structure of History, and inputting information of a transformer, bstruct.hdata is current system time, bstruct.hjy is an insulation resistance value of the transformer, bstruct.hxsb is an absorption ratio of the transformer, bstruct.hjzs is a polarization index of the transformer, bstruct.hbphl is a winding dc resistance imbalance ratio of capacitance and dielectric loss of the transformer, bstruct.hjdsh is a dielectric loss of the transformer, and bstruct.hgzzt is 0;
s502, establishing an array TempArray of four elements, wherein all element values of the array are 0;
s503, performing calculation by using fuzzy neighbor operator FuzzyOperator, where an input of the operator is EStruct, a list BList is History1, and a calculation result of the operator is fuzzy difference variable FuzzyResult; the value of the fuzzy difference variable fuzzy result is stored into the array TempArray [1 ]; sequentially obtaining fuzzy difference variables FuzzyResult of the remaining three lists, and respectively storing the fuzzy difference variables FuzzyResult in an array TempArray [2], an array TempArray [3] and an array TempArray [4 ];
s504, the position of the maximum element in the array in the fuzzy difference maximum FuzzyMaxIndex ═ TempArray;
s505, if the fuzzy difference maximum value FuzzyMaxIndex is equal to 1, outputting that the transformer is in a normal state; if the fuzzy difference maximum, FuzzyMaxIndex, is equal to 2, then output "transformer is in attention state, further attention and inspection is needed"; if the fuzzy difference maximum value FuzzyMaxIndex is equal to 3, outputting that the transformer is in an abnormal state and a fault exists; if the fuzzy difference maximum value FuzzyMaxIndex is equal to 4, then the output "the transformer is in a severe state and a severe fault already exists";
and S506, finishing the diagnosis process and finishing the fault diagnosis of the transformer.
Through the design scheme, the invention can bring the following beneficial effects: the invention relates to a power transformer fault diagnosis method based on fuzzy proximity of state data, which can weigh the difference between a transformer to be diagnosed and a fault transformer in a weighted and fuzzy manner.
Detailed Description
A power transformer fault diagnosis method based on fuzzy proximity of state data comprises the following steps,
step one, inputting a History data table History (hereinafter abbreviated as History) of a power transformer; a normal state list History1 (hereinafter, referred to as History1), a caution state list History2 (hereinafter, referred to as History2), an abnormal state list History3 (hereinafter, referred to as History3) and a serious state list History4 (hereinafter, referred to as History4) are obtained.
The method specifically comprises the following steps of,
s101, inputting a historical data table History of the power transformer, wherein the History comprises the following fields:
data recording time HDATA, insulation resistance value HJY, absorption ratio HXSB, polarization index HJHZS, winding direct-current resistance unbalance rate HBPHL of capacitance and dielectric loss, dielectric loss HJDSH and fault state HGZZT;
s102, selecting all data with a fault state HGZZT equal to a normal state in a History data table History, and storing the data in History1 in an order from small to large according to the value of HDATA;
s103, selecting all data with HGZZT equal to 'attention state' in the History data table History, and storing the data in History2 in sequence from small to large according to the value of HDATA;
s104, selecting all data with HGZZT equal to 'abnormal state' in the History data table History, and storing the data in History3 in sequence from small to large according to the value of HDATA;
s105, selecting all data with the HGZZT being equal to the 'serious state' in the History data table History, and storing the data in History4 in a sequence from small to large according to the value of HDATA;
s106, adding sequential weight fields HSCQZ for History1, History2, History3 and History4 respectively, the default content of the weight field HSCQZ being 0.
And step two, establishing a sequence weight calculation operator SCQZOpersor, wherein the input of the operator is a data list Alist, and the operator calculates and modifies a sequence weight field HSCQZ of the list Alist.
The method specifically comprises the following steps of,
s201, establishing a sequential weight calculation operator SCQZOpersor, wherein the input of the operator is a data list Alist;
s202, sequentially setting a counter SCQZCounter of the weight calculation operator to 1, setting an AList list entry variable SCQZNum to the number of entries of the list AList, and setting the previous dielectric loss PrevJD to AList [1]. hjhjzs, where hjzs is a polarization index and AList [1] is a first element of a list variable of data to be calculated;
s203, sequentially storing a variable SCQZTemp1 ═ tanh (SCQZCounter/SCQZNum) in the weight calculation operator, where tanh is a hyperbolic tangent function, SCQZCounter is the weight calculation operator, and SCQZNum is the number of list entries;
s204, sequentially calculating a temporary storage variable of the weight calculation operator, i.e. two SCQZTemp2 ═ ABS (AList [ scqzoperspective ]. hjhjhzs-PrevJD), where ABS is an absolute value, hjhjzs is a polarization index, PrevJD is a last dielectric loss, AList [ scqzoperspective ] is the scqzoperspective element of the to-be-calculated data list variable;
s205, when the weight calculation operator SCQZCounter is larger than the list entry variable, the calculation process is finished.
Establishing a fuzzy adjacent operator fuzzy operator, inputting an electric transformer state structure body ESTrcuct (hereinafter abbreviated as ESTrcuct) and a list BList to be calculated, and outputting a result as a fuzzy difference variable fuzzy result;
s301, establishing a fuzzy neighbor operator fuzzy operator, wherein the operator inputs an electric transformer state structure body ESTrcuct and a list BList to be calculated, and the field structure of the ESTrcuct is the same as that of a historical data table History;
s302, the counter FuzzyCounter of the fuzzy neighbor operator is 1, the fuzzy neighbor operator entry variable FuzzyNum is the number of entries in the list BList, and the fuzzy accumulation variable FuzzySum is 0;
s303, acquiring a fuzzy temporary storage variable one FuzzyTemp1, a fuzzy temporary storage variable two FuzzyTemp2, a fuzzy temporary storage variable three FuzzyTemp3 and a fuzzy temporary storage variable four FuzzyTemp 4;
s304, acquiring a fuzzy temporary storage variable five FuzzyTemp5 ═ FuzzyTemp1+ FuzzyTemp2+ FuzzyTemp3+ FuzzyTemp 4;
s305, the counter uzzyCounter running to the F fuzzy neighbor is greater than the fuzzy neighbor entry variable FuzzyNum, the fuzzy difference variable FuzzyResult ═ FuzzySum, and the fuzzy difference variable FuzzyResult value is output.
And step four, processing the History1, History2, History3 and History4 by using the sequential weight calculation operators SCQZOpersor.
And fifthly, inputting information of one transformer to construct an information structure BStrect (BStrect for short), and performing fault diagnosis by using fuzzy proximity operators fuzzy operator, History1, History2, History3 and History 4.
The method specifically comprises the following steps of,
s501, constructing an information structure BStruct, where the structure of BStruct is the same as a field structure of History, and inputting information of a transformer, bstruct.hdata is current system time, bstruct.hjy is an insulation resistance value of the transformer, bstruct.hxsb is an absorption ratio of the transformer, bstruct.hjzs is a polarization index of the transformer, bstruct.hbphl is a winding dc resistance imbalance ratio of capacitance and dielectric loss of the transformer, bstruct.hjdsh is a dielectric loss of the transformer, and bstruct.hgzzt is 0;
s502, establishing an array TempArray of four elements, wherein all element values of the array are 0;
s503, performing calculation by using fuzzy neighbor operator FuzzyOperator, where an input of the operator is EStruct, a list BList is History1, and a calculation result of the operator is fuzzy difference variable FuzzyResult; the value of the fuzzy difference variable fuzzy result is stored into the array TempArray [1 ]; sequentially obtaining fuzzy difference variables FuzzyResult of the remaining three lists, and respectively storing the fuzzy difference variables FuzzyResult in an array TempArray [2], an array TempArray [3] and an array TempArray [4 ];
s504, the position of the maximum element in the array in the fuzzy difference maximum FuzzyMaxIndex ═ TempArray;
s505, if the fuzzy difference maximum value FuzzyMaxIndex is equal to 1, outputting that the transformer is in a normal state; if the fuzzy difference maximum, FuzzyMaxIndex, is equal to 2, then output "transformer is in attention state, further attention and inspection is needed"; if the fuzzy difference maximum value FuzzyMaxIndex is equal to 3, outputting that the transformer is in an abnormal state and a fault exists; if the fuzzy difference maximum value FuzzyMaxIndex is equal to 4, then the output "the transformer is in a severe state and a severe fault already exists";
and S506, finishing the diagnosis process and finishing the fault diagnosis of the transformer.
Claims (5)
1. A power transformer fault diagnosis method based on state data fuzzy proximity is characterized by comprising the following steps: comprises the following steps which are sequentially carried out,
step one, inputting a historical data table History of a power transformer into a system, wherein the historical data table History comprises data recording time HDATA, an insulation resistance value HJY, an absorption ratio HXSB, a polarization index HJHZS, a winding direct-current resistance unbalance rate HBPHL of capacitance and dielectric loss, dielectric loss HJDSH and a fault state HGZZT, dividing the fault state HGZZT into four lists according to the level of the fault state HGZZT, and adding a weight field HSCQZ in each list;
step two, establishing a sequence weight calculation operator SCQZOpersor, wherein the input of the operator is a data list variable AList to be calculated, and calculating and modifying a sequence weight field HSCQZ of the data list AList through the operator;
step three, establishing a fuzzy adjacent operator fuzzy operator, wherein the operator inputs a power transformer state structure body ESTrcuct and a list BList to be calculated, and the output result is a fuzzy difference variable fuzzy result;
step four, processing the four lists obtained in the step one and classified according to the HGZZT grade of the fault state by using a sequence weight calculation operator SCQZOpersor;
and fifthly, inputting information of one transformer to construct an information structure BStrect, and performing fault diagnosis by using the fuzzy neighbor operator constructed in the third step and the four fault grade lists after combing in the fourth step.
2. The method for diagnosing the fault of the power transformer based on the fuzzy proximity of the state data as claimed in claim 1, wherein: the specific steps of the first step of dividing into four lists according to the grade of the fault state HGZZT are,
s101, inputting a historical data table History of the power transformer, wherein the History comprises the following fields: data recording time HDATA, insulation resistance value HJY, absorption ratio HXSB, polarization index HJHZS, winding direct-current resistance unbalance rate HBPHL of capacitance and dielectric loss, dielectric loss HJDSH and fault state HGZZT;
s102, selecting all data with a fault state HGZZT equal to a normal state in a History data table History, and storing the data in History1 in an order from small to large according to the value of HDATA;
s103, selecting all data with HGZZT equal to 'attention state' in the History data table History, and storing the data in History2 in sequence from small to large according to the value of HDATA;
s104, selecting all data with HGZZT equal to 'abnormal state' in the History data table History, and storing the data in History3 in sequence from small to large according to the value of HDATA;
s105, selecting all data with the HGZZT being equal to the 'serious state' in the History data table History, and storing the data in History4 in a sequence from small to large according to the value of HDATA;
s106, adding sequential weight fields HSCQZ for History1, History2, History3 and History4 respectively, the default content of the weight field HSCQZ being 0.
3. The method for diagnosing the fault of the power transformer based on the fuzzy proximity of the state data as claimed in claim 1, wherein: the specific operation steps of the second step are as follows,
s201, establishing a sequential weight calculation operator SCQZOpersor, wherein the input of the operator is a data list Alist;
s202, sequentially setting a counter SCQZCounter of the weight calculation operator to 1, setting an AList list entry variable SCQZNum to the number of entries of the list AList, and setting the previous dielectric loss PrevJD to AList [1]. hjhjzs, where hjzs is a polarization index and AList [1] is a first element of a list variable of data to be calculated;
s203, sequentially storing a variable SCQZTemp1 ═ tanh (SCQZCounter/SCQZNum) in the weight calculation operator, where tanh is a hyperbolic tangent function, SCQZCounter is the weight calculation operator, and SCQZNum is the number of list entries;
s204, sequentially calculating a temporary storage variable of the weight calculation operator, i.e. two SCQZTemp2 ═ ABS (AList [ scqzoperspective ]. hjhjhzs-PrevJD), where ABS is an absolute value, hjhjzs is a polarization index, PrevJD is a last dielectric loss, AList [ scqzoperspective ] is the scqzoperspective element of the to-be-calculated data list variable;
s205, when the weight calculation operator SCQZCounter is larger than the list entry variable, the calculation process is finished.
4. The method for diagnosing the fault of the power transformer based on the fuzzy proximity of the state data as claimed in claim 1, wherein: the specific operation steps of the third step are as follows,
s301, establishing a fuzzy neighbor operator fuzzy operator, wherein the operator inputs an electric transformer state structure body ESTrcuct and a list BList to be calculated, and the field structure of the ESTrcuct is the same as that of a historical data table History;
s302, the counter FuzzyCounter of the fuzzy neighbor operator is 1, the fuzzy neighbor operator entry variable FuzzyNum is the number of entries in the list BList, and the fuzzy accumulation variable FuzzySum is 0;
s303, acquiring a fuzzy temporary storage variable one FuzzyTemp1, a fuzzy temporary storage variable two FuzzyTemp2, a fuzzy temporary storage variable three FuzzyTemp3 and a fuzzy temporary storage variable four FuzzyTemp 4;
s304, acquiring a fuzzy temporary storage variable five FuzzyTemp5 ═ FuzzyTemp1+ FuzzyTemp2+ FuzzyTemp3+ FuzzyTemp 4;
s305, the counter uzzyCounter running to the F fuzzy neighbor is greater than the fuzzy neighbor entry variable FuzzyNum, the fuzzy difference variable FuzzyResult ═ FuzzySum, and the fuzzy difference variable FuzzyResult value is output.
5. The method for diagnosing the fault of the power transformer based on the fuzzy proximity of the state data as claimed in claim 1, wherein: the concrete operation steps of the step five are as follows,
s501, constructing an information structure BStruct, where the structure of BStruct is the same as a field structure of History, and inputting information of a transformer, bstruct.hdata is current system time, bstruct.hjy is an insulation resistance value of the transformer, bstruct.hxsb is an absorption ratio of the transformer, bstruct.hjzs is a polarization index of the transformer, bstruct.hbphl is a winding dc resistance imbalance ratio of capacitance and dielectric loss of the transformer, bstruct.hjdsh is a dielectric loss of the transformer, and bstruct.hgzzt is 0;
s502, establishing an array TempArray of four elements, wherein all element values of the array are 0;
s503, performing calculation by using fuzzy neighbor operator FuzzyOperator, where an input of the operator is EStruct, a list BList is History1, and a calculation result of the operator is fuzzy difference variable FuzzyResult; the value of the fuzzy difference variable fuzzy result is stored into the array TempArray [1 ]; sequentially obtaining fuzzy difference variables FuzzyResult of the remaining three lists, and respectively storing the fuzzy difference variables FuzzyResult in an array TempArray [2], an array TempArray [3] and an array TempArray [4 ];
s504, the position of the maximum element in the array in the fuzzy difference maximum FuzzyMaxIndex ═ TempArray;
s505, if the fuzzy difference maximum value FuzzyMaxIndex is equal to 1, outputting that the transformer is in a normal state; if the fuzzy difference maximum, FuzzyMaxIndex, is equal to 2, then output "transformer is in attention state, further attention and inspection is needed"; if the fuzzy difference maximum value FuzzyMaxIndex is equal to 3, outputting that the transformer is in an abnormal state and a fault exists; if the fuzzy difference maximum value FuzzyMaxIndex is equal to 4, then the output "the transformer is in a severe state and a severe fault already exists";
and S506, finishing the diagnosis process and finishing the fault diagnosis of the transformer.
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