CN113945299A - Method for predicting temperature distribution and residual life of transformer winding - Google Patents
Method for predicting temperature distribution and residual life of transformer winding Download PDFInfo
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Abstract
The invention belongs to the technical field of power transformer state evaluation, and particularly relates to a method for predicting the temperature distribution and the residual life of a transformer winding, which directly measures the temperature of discrete points of a winding in a transformer by a plurality of groups of fiber bragg grating temperature sensors, combines a neural network algorithm to fit continuous data among the windings, predicts the temperature distribution of each cake of the transformer winding, predicts the temperature of each region position, and extracts and approaches the nonlinear relation between input temperature and output temperature by learning; according to the acquired transformer hot spot temperature, the aging rate and the residual life of the transformer are predicted by utilizing a thermal aging 6-degree method; the result obtained by the method is beneficial to adjusting the load condition of the transformer and warning the abnormal condition of the transformer, is convenient to find the transformer under the abnormal working condition, is beneficial to ensuring the safe and stable operation of the power transformer, and has popularization prospect.
Description
Technical Field
The invention belongs to the technical field of power transformer state evaluation, and particularly relates to a method for predicting the temperature distribution and the residual life of a transformer winding.
Background
The transformer is one of the core devices of the power grid, and plays an important role in power transmission. When the transformer runs, the loss generated by components such as an iron core and a winding can generate temperature rise inside, so that the insulation aging of the winding of the transformer is accelerated, and the service life of the transformer is further influenced. The load capacity and the remaining life of the transformer are mainly dependent on its thermal characteristics. Therefore, in order to prevent the transformer from overheating and ensure the safe and stable operation of the transformer, it is necessary to research a method for predicting the temperature and the service life of the transformer.
At present, the conventional method for acquiring the temperature of the internal winding of the transformer mainly comprises a direct measurement method, a thermal simulation measurement method, an indirect calculation method and the like. According to the direct measurement method, the sensors are embedded in the transformer winding, the temperature of the winding measurement point is obtained by using the temperature measuring instrument, and the more the embedded temperature sensors are, the more accurate the measurement result is. But because the heat dissipation inside the transformer is uneven, it is difficult to ensure that the installation of the sensor can be placed at the hot spot temperature position of the winding a priori. The thermal simulation measurement method superposes the temperature rise of the additional current on the electric heating element on the top oil temperature of the transformer to obtain the hot spot temperature of the transformer winding. The method has large measurement error and poor practicability. The indirect calculation method is mainly used for calculating the winding hot spot temperature by calculating each key parameter and the load current value of the transformer according to the transformer and the environmental parameters which are easy to measure in actual operation. Because the simplified hot-circuit model does not consider many influencing factors, such as: viscosity coefficient of oil, load dynamic loss, etc., so that the accuracy of the calculation result cannot be guaranteed. Therefore, it is desirable to provide an accurate method for predicting the temperature distribution of the transformer winding and evaluating the remaining life.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for predicting the temperature distribution and the residual life of a transformer winding, which combines a conventional optical fiber temperature measurement method and a temperature distribution prediction algorithm, has the advantages of high calculation speed, high temperature distribution prediction precision, accurate transformer aging rate and service life prediction, is convenient for rapidly finding a transformer under an abnormal working condition, and is beneficial to ensuring the safe and stable operation of a power transformer. The specific technical scheme of the invention is as follows:
a method for predicting the temperature distribution and the residual life of a transformer winding comprises the following steps:
s1, collecting operation parameters and temperature rise test data of a transformer with the same model as the transformer to be tested;
s2, after the transformer to be measured finishes the manufacture of the iron core and the winding of the winding, burying a plurality of fiber bragg grating temperature sensors between turns of the winding of the transformer according to a specific rule, and measuring the temperature data of each discrete position of the winding in real time;
s3, based on the temperature data of each discrete position of the winding obtained by real-time measurement, predicting the temperature data of each area inside the winding by combining a neural network algorithm, and predicting the temperature distribution of each cake of the transformer winding;
and S4, predicting the aging rate and the residual life of the transformer by using a thermal aging 6-degree method according to the temperature distribution predicted value of each cake of the transformer winding obtained in the S3.
Preferably, in the step S1, the transformers of the same type are power transformers of the same specification which are already put into operation.
Preferably, the operation parameters and the temperature rise test data in step S1 include parameters of the transformer, temperature of the top layer oil, temperature of the inlet and outlet of the radiator, and winding hot spot temperature information.
Preferably, the parameters of the transformer comprise the height and the diameter of a transformer coil, the length, the width and the height of an iron core from a side yoke, and the dimension of the three-dimensional circumference of an oil tank.
Preferably, the specific rule in step S2 is: the part 10% -20% below the top of the transformer winding is a local hot spot area, and the number of the fiber bragg grating temperature sensors embedded in each cake coil in the local hot spot area is more than that of the fiber bragg grating temperature sensors embedded in each cake coil in other conventional temperature areas.
Preferably, the embedding method of the fiber grating temperature sensor comprises the following steps: and embedding and fixing the tail fibers in the insulation block grooves along gaps among the cakes on the top of the transformer winding, and connecting the tail fibers of the temperature measuring channel into the through plate from the top of the winding.
Preferably, the step S3 specifically includes the following steps:
let x1,x2,...,xnThe temperature data of each discrete position of the winding measured by the fiber bragg grating temperature sensor is used as the input of the neural network, and the total number of the temperature data input into the neural network is n; let y1,y2,...,ymThe transformer winding temperature pre-measurement is output after the neuron calculation, and the total output number after the neuron calculation is m; w is aijIs the connection weight value between the ith neuron of the input layer and the jth neuron of the hidden layer, wjkThe connection weight value between the jth neuron of the hidden layer and the kth neuron of the output layer is obtained;
the weighted sum of the jth temperature input of the intermediate hidden layer is then:
for hidden layer neurons, the excitation function is:
for the implied layer jth neuron there are:
Hjis the output of the jth neuron of the hidden layer, and the calculation formula is as follows:
Hj＝f(netj)；(3)
the output error is then:
l is the total number of hidden layer neurons.
Wherein, YkOutputting expectation for the neuron, and obtaining the expectation based on the operation parameters and the temperature rise test data of the transformer with the same model collected in the step S1;
based on the output error, the output layer and the hidden layer are corrected as follows:
and (3) output layer weight correction:
wjk2＝wjk+ηHjek；(5)
wherein eta is a learning rate and is a network parameter of the neural network; w is ajk2The modified value of the connection weight between the jth neuron of the hidden layer and the kth neuron of the output layer is obtained;
and (3) hidden layer weight correction:
wherein, wij2The correction value of the connection weight between the ith neuron of the input layer and the jth neuron of the hidden layer is obtained;
based on the corrected output layer weight and the hidden layer weight, the predicted temperature distribution of each cake of the transformer winding can be obtained:
wherein Hj2Represents the modified hidden layer jth neuron,
preferably, the step S4 of predicting the aging rate and the remaining life of the transformer by using the thermal aging 6 degree method specifically includes:
the thermal aging 6-degree method means that the aging rate is doubled every time the internal temperature of the transformer rises by 6K, and based on the aging rate, the relative aging rate at each hot point temperature is obtained as follows:
V＝2(t-98)/6；(8)
wherein t is the internal temperature of the transformer, and the unit is centigrade degree;
the remaining life of the transformer is obtained by using the lost life:
wherein, L is the residual life of the transformer; s is the design life of the transformer; v is that the transformer operates at t1～t2Relative rate of aging over a period of time.
The invention has the beneficial effects that: on one hand, the temperature of discrete points of a winding in a transformer is directly measured through a plurality of groups of fiber bragg grating temperature sensors, continuous data among windings are fitted by combining a neural network algorithm, the temperature distribution of each cake of the winding of the transformer is predicted, wherein the neural network algorithm is used for fitting the temperature data of the discrete points and predicting the continuous temperature distribution of each area of the winding in the transformer, each area of the winding is that each cake of the winding is divided into a plurality of areas, the temperature prediction is carried out on the position of each area, and the nonlinear relation between the input temperature and the output temperature is extracted and approximated through learning; on the other hand, according to the acquired hot spot temperature of the transformer, the aging rate and the residual life of the transformer are predicted by using a thermal aging 6-degree method; the result obtained by the method is beneficial to adjusting the load condition of the transformer and warning the abnormal condition of the transformer, is convenient to find the transformer under the abnormal working condition, is beneficial to ensuring the safe and stable operation of the power transformer, and has popularization prospect.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flowchart of a method for predicting a temperature distribution and a remaining life of a transformer winding based on optical fiber sensing according to an embodiment of the present invention;
FIG. 2 is a detailed view of the burying of the measuring point of the optical fiber temperature sensor in the present invention;
FIG. 3 is a schematic diagram of a conventional temperature zone array and a hot spot temperature zone array of the fiber optic temperature sensor of the present invention;
FIG. 4 is a neural network architecture of the present invention;
description of reference numerals: 1. the device comprises a winding tenth cake, a winding ninth cake, a winding eighth cake, a winding seventh cake, a winding sixth cake, a winding fifth cake, a winding fourth cake, a winding third cake, a winding seventh cake, a winding sixth cake, a winding fifth cake, a winding fourth cake, a winding third cake, a winding seventh cake, a winding second cake, a winding sixth cake, a winding first cake, a winding fifth cake, a winding fourth cake, a winding third cake, a winding seventh cake, a winding sixth cake, a winding third cake, a winding fourth cake, a winding first cake, a winding temperature area array, a sensor 12, a sensor conventional temperature area array, a sensor hot spot temperature area array, a sensor hot spot temperature area array, 14 and an array connector.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. 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 invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, a method for predicting the temperature distribution and the remaining life of a transformer winding includes the following steps:
s1, collecting operation parameters and temperature rise test data of a transformer with the same model as the transformer to be tested; the transformers in the same type are power transformers with the same specification and already put into operation. The operation parameters and the temperature rise test data comprise parameters of the transformer, oil temperature of a top layer, inlet and outlet temperatures of a radiator and winding hot spot temperature information. The parameters of the transformer comprise the height and the diameter of a transformer coil, the length, the width and the height of an iron core from a side yoke, and the dimension of the three-dimensional area of an oil tank.
And S2, after the transformer to be measured finishes the manufacture of the iron core and the winding of the winding, burying a plurality of fiber bragg grating temperature sensors between turns of the winding of the transformer according to a specific rule, and measuring the temperature data of each discrete position of the winding in real time. The specific rule is: the part 10% -20% below the top of the transformer winding is a local hot spot area, and the number of the fiber bragg grating temperature sensors embedded in each cake coil in the local hot spot area is more than that of the fiber bragg grating temperature sensors embedded in each cake coil in other conventional temperature areas. As shown in fig. 2, according to the specific rule, a plurality of fiber bragg grating sensors 11 are buried between winding turns of the transformer, and temperature data of each discrete position of the winding is measured in real time; the winding is wound by 10 in a co-winding mode and comprises a tenth winding cake 1, a ninth winding cake 2, an eighth winding cake 3, a seventh winding cake 4, a sixth winding cake 5, a fifth winding cake 6, a fourth winding cake 7, a third winding cake 8, a second winding cake 9 and a first winding cake 10. Specifically, 2 sensors are embedded in each cake of a local hot spot area, 1 sensor is embedded in each cake of a conventional temperature area, the fiber bragg grating sensors form a sensor array for measuring the temperature of the high/low voltage winding, the high/low voltage winding respectively corresponds to 1 sensor conventional temperature area array 12 and 1 sensor hot spot temperature area array 13, as shown in fig. 3, the diameter of the fiber bragg grating sensor is 3mm, the tail fiber is made of polytetrafluoroethylene, an FC/APC connector is adopted, and the connector of each array is an array connector 14. The embedding method of the fiber grating temperature sensor comprises the following steps: and embedding and fixing the tail fibers in the insulation block grooves along gaps among the cakes on the top of the transformer winding, and connecting the tail fibers of the temperature measuring channel into the through plate from the top of the winding.
And S3, predicting the temperature data of each area inside the winding and predicting the temperature distribution of each cake of the transformer winding by combining a neural network algorithm based on the temperature data of each discrete position of the winding obtained by real-time measurement. The method specifically comprises the following steps:
let x1,x2,...,xnThe temperature data of each discrete position of the winding measured by the fiber bragg grating temperature sensor is used as the input of the neural network, and the number is n; let y1,y2,...,ymThe calculated and output transformer winding temperature pre-measurement is carried out on the neurons, and the number of the transformer winding temperature pre-measurement is m; w is aijIs the connection weight value between the ith neuron of the input layer and the jth neuron of the hidden layer, wjkThe connection weight value between the jth neuron of the hidden layer and the kth neuron of the output layer is obtained;
the weighted sum of the jth temperature input of the intermediate hidden layer is then:
for hidden layer neurons, the excitation function is:
for the implied layer jth neuron there are:
Hjis the output of the jth neuron of the hidden layer, and the calculation formula is as follows:
Hj＝f(netj)；(3)
the output error is then:
l is the total number of hidden layer neurons.
Wherein, YkOutputting expectation for the neuron, and obtaining the expectation based on the operation parameters and the temperature rise test data of the transformer with the same model collected in the step S1;
based on the output error, the output layer and the hidden layer are corrected as follows:
and (3) output layer weight correction:
wjk2＝wjk+ηHjek；(5)
wherein eta is a learning rate and is a network parameter of the neural network; w is ajk2The modified value of the connection weight between the jth neuron of the hidden layer and the kth neuron of the output layer is obtained;
and (3) hidden layer weight correction:
wherein, wij2The correction value of the connection weight between the ith neuron of the input layer and the jth neuron of the hidden layer is obtained;
based on the corrected output layer weight and the hidden layer weight, the predicted temperature distribution of each cake of the transformer winding can be obtained:
ykthe method comprises the steps of calculating and predicting the temperature of a winding inside a transformer through a neural network, equally dividing each cake of the winding into a plurality of areas, and predicting the temperature of each area; k is the number of each area of the transformer winding, such as: mixing all the cakesDividing the cake into 4 areas, wherein k of a first area of the first cake is 1, k of a second area of the first cake is 2, k of a third area of the first cake is 3, and k of a fourth area of the first cake is 4;
k is 5 in the first area of the second cake, k is 6 in the second area of the second cake, k is 7 in the third area of the second cake, k is 8 in the fourth area of the second cake, and so on.
Wherein Hj2Represents the modified hidden layer jth neuron,
in the above algorithm, the neural network algorithm is used to fit the temperature data of the discrete points and predict the continuous temperature distribution of each region of the internal winding of the transformer. The winding areas are obtained by equally dividing winding cakes into a plurality of areas, predicting the temperature of the area positions, and extracting and approaching the nonlinear relation between the input temperature and the output temperature through learning. And the continuous temperature distribution of each position of the winding means that each cake of the winding is divided into a plurality of areas, and the temperature of each area position of each cake is predicted.
And S4, predicting the aging rate and the residual life of the transformer by using a thermal aging 6-degree method according to the temperature distribution predicted value of each cake of the transformer winding obtained in the S3.
The thermal aging 6-degree method means that the aging rate is doubled every time the internal temperature of the transformer rises by 6K, and based on the aging rate, the relative aging rate at each hot point temperature is obtained as follows:
V＝2(t-98)/6；(8)
wherein t is the internal temperature of the transformer in degrees celsius.
The remaining life of the transformer is obtained by using the lost life:
wherein, L is the residual life of the transformer; s is the design life of the transformer; v is that the transformer operates at t1～t2Relative rate of aging over a period of time.
In addition, the fiber bragg grating temperature sensor is buried outside the winding insulation, and the measured temperature is the temperature of the insulation layer close to the lead. According to the heat conduction mechanism of heat transfer science, the surface of a copper wire or an aluminum wire and the outer surface of insulating paper have temperature difference. For this, the measurement value correction formula is as follows:
wherein q is the surface heat flux density of the winding; lambda is the thermal conductivity of the insulating layer; delta is the thickness of the insulating layer; t isrealThe real temperature value is obtained; t istestIs the measured value of the fiber grating temperature sensor.
On one hand, the temperature of discrete points of a winding in a transformer is directly measured through a plurality of groups of fiber bragg grating sensors, continuous data among windings are fitted by combining a neural network algorithm, the temperature distribution of each cake of the winding of the transformer is predicted, wherein the neural network algorithm is used for fitting the temperature data of the discrete points and predicting the continuous temperature distribution of each area of the winding in the transformer, each area of the winding is that each cake of the winding is divided into a plurality of areas, the temperature prediction is carried out on the position of each area, and the nonlinear relation between the input temperature and the output temperature is extracted and approximated through learning; on the other hand, according to the acquired hot spot temperature of the transformer, the aging rate and the residual life of the transformer are predicted by using a thermal aging 6-degree method; the obtained result is beneficial to adjusting the load condition of the transformer and warning the abnormal condition of the transformer, is convenient to find the transformer under the abnormal working condition, is beneficial to ensuring the safe and stable operation of the power transformer, and has popularization prospect.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the components of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the division of the unit is only one division of logical functions, and other division manners may be used in actual implementation, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. A method for predicting the temperature distribution and the residual life of a transformer winding is characterized by comprising the following steps: the method comprises the following steps:
s1, collecting operation parameters and temperature rise test data of a transformer with the same model as the transformer to be tested;
s2, after the transformer to be measured finishes the manufacture of the iron core and the winding of the winding, burying a plurality of fiber bragg grating temperature sensors between turns of the winding of the transformer according to a specific rule, and measuring the temperature data of each discrete position of the winding in real time;
s3, based on the temperature data of each discrete position of the winding obtained by real-time measurement, predicting the temperature data of each area inside the winding by combining a neural network algorithm, and predicting the temperature distribution of each cake of the transformer winding;
and S4, predicting the aging rate and the residual life of the transformer by using a thermal aging 6-degree method according to the temperature distribution predicted value of each cake of the transformer winding obtained in the S3.
2. The method for predicting the temperature distribution and the residual life of the transformer winding according to claim 1, wherein: in the step S1, the transformers of the same type are power transformers of the same specification and already put into operation.
3. The method for predicting the temperature distribution and the residual life of the transformer winding according to claim 1, wherein: the operation parameters and the temperature rise test data in the step S1 include parameters of the transformer, the temperature of the top oil, the temperature of the inlet and the outlet of the radiator, and the temperature information of the winding hot spot.
4. The method for predicting the temperature distribution and the residual life of the transformer winding according to claim 3, wherein: the parameters of the transformer comprise the height and the diameter of a transformer coil, the length, the width and the height of an iron core from a side yoke, and the dimension of the three-dimensional area of an oil tank.
5. The method for predicting the temperature distribution and the residual life of the transformer winding according to claim 1, wherein: the specific rule in the step S2 is: the part 10% -20% below the top of the transformer winding is a local hot spot area, and the number of the fiber bragg grating temperature sensors embedded in each cake coil in the local hot spot area is more than that of the fiber bragg grating temperature sensors embedded in each cake coil in other conventional temperature areas.
6. The method for predicting the temperature distribution and the residual life of the transformer winding according to claim 5, wherein: the embedding method of the fiber grating temperature sensor comprises the following steps: and embedding and fixing the tail fibers in the insulation block grooves along gaps among the cakes on the top of the transformer winding, and connecting the tail fibers of the temperature measuring channel into the through plate from the top of the winding.
7. The method for predicting the temperature distribution and the residual life of the transformer winding according to claim 1, wherein: the step S3 specifically includes the following steps:
let x1,x2,...,xnThe temperature data of each discrete position of the winding measured by the fiber bragg grating temperature sensor is used as the input of the neural network, and the number is n; let y1,y2,...,ymThe calculated and output transformer winding temperature pre-measurement is carried out on the neurons, and the number of the transformer winding temperature pre-measurement is m; w is aijIs the connection weight value between the ith neuron of the input layer and the jth neuron of the hidden layer, wjkThe connection weight value between the jth neuron of the hidden layer and the kth neuron of the output layer is obtained;
the weighted sum of the jth temperature input of the intermediate hidden layer is then:
for hidden layer neurons, the excitation function is:
for the implied layer jth neuron there are:
Hjis the output of the jth neuron of the hidden layer, and the calculation formula is as follows:
Hj＝f(netj)； (3)
the output error is then:
l is the total number of hidden layer neurons.
Wherein, YkOutputting expectation for the neuron, and obtaining the expectation based on the operation parameters and the temperature rise test data of the transformer with the same model collected in the step S1;
based on the output error, the output layer and the hidden layer are corrected as follows:
and (3) output layer weight correction:
wjk2＝wjk+ηHjek； (5)
wherein eta is a learning rate and is a network parameter of the neural network; w is ajk2The modified value of the connection weight between the jth neuron of the hidden layer and the kth neuron of the output layer is obtained;
and (3) hidden layer weight correction:
wherein, wij2The correction value of the connection weight between the ith neuron of the input layer and the jth neuron of the hidden layer is obtained;
based on the corrected output layer weight and the hidden layer weight, the predicted temperature distribution of each cake of the transformer winding can be obtained:
wherein, ykIs the predicted temperature of the inner winding of the transformer after being calculated by a neural network, k is the number of each area of the transformer winding, Hj2Represents the modified hidden layer jth neuron,
8. the method for predicting the temperature distribution and the residual life of the transformer winding according to claim 1, wherein: the step S4 of predicting the aging rate and the remaining life of the transformer by using the thermal aging 6 degree method specifically includes: the thermal aging 6-degree method means that the aging rate is doubled every time the internal temperature of the transformer rises by 6K, and based on the aging rate, the relative aging rate at each temperature is obtained as follows:
V＝2(t-98)/6； (8)
the remaining life of the transformer is obtained by using the lost life:
wherein, L is the residual life of the transformer; s is the design life of the transformer; v is that the transformer operates at t1～t2Relative rate of aging over a period of time.
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| CN202111078692.8A CN113945299A (en) | 2021-09-15 | 2021-09-15 | Method for predicting temperature distribution and residual life of transformer winding |
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