CN109635397A - A kind of method of determining Self-cooling oil-immersed transformer thermal driving force - Google Patents
A kind of method of determining Self-cooling oil-immersed transformer thermal driving force Download PDFInfo
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Abstract
The invention discloses a kind of methods of determining Self-cooling oil-immersed transformer thermal driving force, the following steps are included: obtaining load current, environment temperature and the dependency structure parameter transitivity parameter of transformer, explicit oil temperature difference prediction model is established based on genetic programming, the oil temperature difference data of prediction is substituted into thermal driving force model and obtains Self-cooling oil-immersed transformer oil stream thermal driving force.The beneficial effects of the present invention are, only need the load current for monitoring transformer and environment temperature that the key parameters in thermal driving force calculating can be obtained: top-oil temperature and the oil temperature of bottom oil temperature are poor, without to the analytical calculation that thermal driving force can be realized in fortune transformer installation bottom oil temperature monitoring device, reduce drain on manpower and material resources, it increases economic efficiency, provides convenience for transformer Cooling effect analysis and operation and maintenance.
Description
Technical field
The present invention relates to electric insulation on-line checking and fault diagnosis field, especially a kind of determining Self-cooling oil immersed type becomes
The method of depressor thermal driving force.
Background technique
There is the loss of copper loss, iron loss and other forms at runtime and appear in transformer in the form of heat in transformer
Inside.These heats need to be dispersed in time by cooling system, and otherwise the service life of inside transformer insulating materials will contract
It is short, influence the safe operation of transformer.The cooling of Self-cooling oil-immersed transformer relies on the natural circulation of transformer oil,
In oil stream cooling circuit, what the power of oil circulation was generated all from the variation of autologous density caused by the temperature difference in closed circuit
Thermal driving force, so the analytical calculation of oil stream thermal driving force has important value to assessment transformer cooling system effect.
The existing method of thermal driving force analytical calculation is top-oil temperature, the bottom that transformer radiator is monitored using sensor
Oil temperature and ambient temperature data substitute into calculation formula and obtain thermal driving force.But for the transformation in most engineering applications in fortune
For device, not preparatory installing bottom layer oil temperature monitors sensor when factory.It is driven to the above-mentioned heat in fortune transformer of analytical calculation
Power, it is necessary to be transformed the operation of transformer temporary withdrawal.This measure will cause a degree of economic loss and manpower and material resources
Waste, and exist due to sensor Rig up error influence transformer original cooling and insulation performance risk.Therefore, in transformation
During device operates normally, if the relevant parameter of transformer device structure and physical property and the indirect calculating hand of oil temperature can be efficiently used
The calculating of Duan Shixian thermal driving force is analyzed, and can avoid reducing the transformation link when transporting transformer thermal driving force and calculating analysis
Drain on manpower and material resources is increased economic efficiency, and provides convenience for the analysis and operation and maintenance of transformer Cooling effect.
Summary of the invention
In view of the above technical problems, the purpose of the present invention is to propose to a kind of determining Self-cooling oil-immersed transformer thermal driving forces
Method, need to only obtain the load current of oil-immersed transformer of an installing fibre optical sensor, top-oil temperature, bottom oil temperature and
Ambient temperature data carries out driving modeling to data using genetic programming, can establish the explicit prediction of oil temperature difference of the transformer
Model, and then the thermal driving force of cooling circuit oil stream is acquired, realize equivalent capability model oil-immersed transformer oil in electric system
Flow the analytical calculation of thermal driving force.
Realize that the technical solution of the object of the invention is as follows:
A kind of method of determining Self-cooling oil-immersed transformer thermal driving force, includes the following steps:
The first step, the oil stream density p for obtaining Self-cooling oil-immersed transformeroil, oil stream coefficient of expansion βoil, Cool Hot Core it is high-order
Poor Δ h；
Second step utilizes thermal driving force Δ pdComputation model calculates the oil stream in Self-cooling oil-immersed transformer closed circuit
Thermal driving force, model are as follows:
Δpd=ρoilgβoilΔhΔθ
In formula, g is acceleration of gravity, and Δ θ is that top-oil temperature and the oil temperature of bottom oil temperature are poor；
The oil temperature difference θ predicts mould by monitoring Transformer load current and environment temperature, in conjunction with oil temperature difference
Type is calculated；
The acquisition pattern of the oil temperature difference prediction model includes the following steps:
(1) top-oil temperature, the bottom oil temperature, environment of the oil-immersed transformer actual measurement of an installing optical fiber temperature-measurement equipment are obtained
Top-oil temperature is further subtracted bottom oil temperature and obtains oil temperature difference data by temperature, load current data；
(2) basic framework for setting oil-immersed transformer oil temperature difference prediction model is as follows:
In formula, IpuFor load factor, θambIndicate environment temperature, t indicates time variable；
(3) differential value of the oil temperature difference data obtained in (1) in time is calculatedAnd it is load current is more specified than upper
Electric current obtains load factor Ipu(k), acquired ambient temperature data θamb(k) constant, wherein k indicates discrete-time variable, i.e.,
The data obtained is one group of discrete value that sampling obtains.Further by oil temperature difference differential value, oil temperature is poor, load factor and environment temperature
Data are divided into training set and forecast set；
(4) driving modeling is carried out to training set data using genetic programming algorithm, establishes explicit oil temperature difference prediction model,
It is specific as follows:
1) initialization population: setting genetic programming algorithm operational parameter control, being generated at random by algorithm has Z function
The population primary of body；
The control parameter of algorithm includes the function individual amount Z of population, training algebra G, meets genetic programming termination rule
Then set threshold gamma, function individual maximum node number Nm, fitness function weight coefficient α1And α2, crossover probability PcAnd variation is general
Rate PmInitial value, collection of functions, leaf node；
2) function individual adaptation degree size is calculated by fitness function based on training set data, fitness value is smaller, letter
Several bodies are more excellent；
The fitness function for calculating function individual adaptation degree size is provided that
In formula, Jg,iIt is g for the calculated value of i-th of function individual in population, R (k) is the differential of oil temperature difference in training set
Value, M are training set size, i.e. the data group number of training set, and N is the number of nodes of function individual, εmaxFor the calculating of current function individual
Maximum mean absolute error, α1And α2For fitness function weight coefficient；
3) it selects: the individual of the function for carrying out genetic manipulation is selected by roulette method；
4) genetic manipulation is executed to the function individual selected, generates population of lower generation；
The genetic manipulation includes the intersection and variation of function individual, wherein crossover probability Pc, mutation probability PmWill with into
Change iteration constantly adaptive change, function individual adaptation degree is smaller, and assigned intersection, mutation probability are bigger；
5) above is repeated 2) to the 4) step, until meeting algorithm termination rules；
The algorithm termination rules, specifically:
1. the difference of adjacent generations maximum adaptation angle value reaches preset threshold gamma, it may be assumed that
|Fmax(Jg,i+1)-Fmax(Jg,i)|≤γ
F in formulamax(Jg,i+1) and Fmax(Jg,i) be respectively adjacent generations maximum adaptation angle value；
2. evolving to predetermined trained algebra G；
Meeting the above wherein rule is the modeling operational process for terminating genetic programming algorithm；
6) using the smallest function individual of fitness value in last generation of genetic programming algorithm as oil temperature difference prediction model；
(5) load factor, environment temperature in forecast set are inputted into oil temperature difference prediction model, obtain the predicted value of oil temperature difference,
And it utilizes following formula to calculate the mean absolute error MAE of oil temperature difference measured value in oil temperature difference predicted value and forecast set, averagely miss relatively
Poor MRE and goodness of fit R2, verify model prediction accuracy and accuracy；
In formula, n is data points, fiFor the oil temperature difference predicted value obtained by prediction model, yiFor the oil temperature in forecast set
Poor measured value,For the average value of oil temperature difference in forecast set；
(6) if the precision of prediction for the model that forecast set is verified in (5) is up to standard, it is poor as final oil temperature to select the model
Prediction model；If its precision of prediction is not up to standard, (4), (5) are repeated, it is pre- until selecting precision of prediction final oil temperature difference up to standard
Survey model；
The criterion whether precision of prediction is up to standard is specifically set are as follows: if mean absolute error MAE less than 2 DEG C, is put down
Equal relative error MRE is less than 1.20% and goodness of fit R2Greater than 0.9, then precision of prediction is up to standard, is otherwise considered as precision of prediction not
It is up to standard.
The beneficial effects of the present invention are a kind of methods of determining Self-cooling oil-immersed transformer thermal driving force, it is only necessary to supervise
The key parameters in thermal driving force calculating: top-oil temperature and bottom oil can be obtained in the load current and environment temperature for surveying transformer
The oil temperature of temperature is poor, without subtracting to the analytical calculation that thermal driving force can be realized in fortune transformer installation bottom oil temperature monitoring device
Few drain on manpower and material resources, increases economic efficiency, provides convenience for transformer Cooling effect analysis and operation and maintenance.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the method for determining Self-cooling oil-immersed transformer thermal driving force of the present invention；
Specific embodiment
Invention is further explained with specific implementation process with reference to the accompanying drawing.
The first step, the oil stream density p for obtaining Self-cooling oil-immersed transformeroil, oil stream coefficient of expansion βoil, Cool Hot Core it is high-order
The oily temperature difference θ of poor Δ h and top-oil temperature and bottom oil temperature；The oil temperature difference θ passes through monitoring Transformer load electricity
Stream and environment temperature, are calculated in conjunction with oil temperature difference prediction model；The acquisition pattern of oil temperature difference prediction model includes the following steps:
(1) top-oil temperature, the bottom oil temperature, environment of the oil-immersed transformer actual measurement of an installing optical fiber temperature-measurement equipment are obtained
Top-oil temperature is further subtracted bottom oil temperature and obtains oil temperature difference data by temperature, load current data；
(2) basic framework for setting oil-immersed transformer oil temperature difference prediction model is as follows:
In formula (1), IpuFor load factor, θambIndicate environment temperature, t indicates time variable；
(3) differential value of oil temperature difference data in time acquired in the first step is calculatedAnd by load current than upper
Rated current obtains load factor Ipu(k), acquired ambient temperature data θamb(k) constant, wherein k indicates that discrete time becomes
Amount, i.e. the data obtained are one group of discrete value that sampling obtains.Further by oil temperature difference differential value, oil temperature is poor, load factor and ring
Border temperature data is divided into training set and forecast set；
(4) driving modeling is carried out to training set data using genetic programming algorithm, establishes explicit oil temperature difference prediction model,
It is specific as follows:
1) initialization population: setting genetic programming algorithm operational parameter control, being generated at random by algorithm has Z function
The population primary of body；
The control parameter of algorithm includes the function individual amount Z of population, training algebra G, meets genetic programming termination rule
Then set threshold gamma, function individual maximum node number Nm, fitness function weight coefficient α1And α2, crossover probability PcAnd variation is general
Rate PmInitial value, collection of functions, leaf node；
2) function individual adaptation degree size is calculated by fitness function based on training set data, fitness value is smaller, letter
Several bodies are more excellent；
The fitness function for calculating function individual adaptation degree size is provided that
In formula (2), Jg,iIt is g for the calculated value of i-th of function individual in population, R (k) is oil temperature difference in training set
Differential value, M are training set size, i.e. the data group number of training set, and N is the number of nodes of function individual, εmaxFor current function individual
The maximum mean absolute error of calculating, α1And α2For fitness function weight coefficient；
3) it selects: the individual of the function for carrying out genetic manipulation is selected by roulette method；
4) genetic manipulation is executed to the function individual selected, generates population of lower generation；
The genetic manipulation includes the intersection and variation of function individual, wherein crossover probability Pc, mutation probability PmWill with into
Change iteration constantly adaptive change, function individual adaptation degree is smaller, and assigned intersection, mutation probability are bigger；
5) above is repeated 2) to the 4) step, until meeting algorithm termination rules；
The algorithm termination rules, specifically:
1. the difference of adjacent generations maximum adaptation angle value reaches preset threshold gamma, it may be assumed that
|Fmax(Jg,i+1)-Fmax(Jg,i)|≤γ (3)
F in formula (3)max(Jg,i+1) and Fmax(Jg,i) be respectively adjacent generations maximum adaptation angle value；
2. evolving to predetermined trained algebra G；
Meeting the above wherein rule is the modeling operational process for terminating genetic programming algorithm；
6) using the smallest function individual of fitness value in last generation of genetic programming algorithm as oil temperature difference prediction model；
(5) load factor, environment temperature in forecast set are inputted into oil temperature difference prediction model, obtain the predicted value of oil temperature difference,
And using following formula calculate in oil temperature difference predicted value and forecast set mean absolute error MAE, the average relative error MRE of measured value and
Goodness of fit R2, verify model prediction accuracy and accuracy；
In formula (4), n is data points, fiFor the oil temperature difference predicted value obtained by prediction model, yiIt is oily in forecast set
The measured value of the temperature difference,For the average value of oil temperature difference in forecast set；
(6) if the precision of prediction for the model that forecast set is verified in (5) is up to standard, it is poor as final oil temperature to select the model
Prediction model；If its precision of prediction is not up to standard, (4), (5) are repeated, it is pre- until selecting precision of prediction final oil temperature difference up to standard
Survey model；
The criterion whether precision of prediction is up to standard is specifically set are as follows: if mean absolute error MAE less than 2 DEG C, is put down
Equal relative error MRE is less than 1.20% and goodness of fit R2Greater than 0.9, then precision of prediction is up to standard, is otherwise considered as precision of prediction not
It is up to standard.
Second step utilizes thermal driving force Δ pdComputation model calculates the oil stream in Self-cooling oil-immersed transformer closed circuit
Thermal driving force, model are as follows:
Δpd=ρoilgβoilΔhΔθ (5)
In formula (5), g is acceleration of gravity.
Claims (1)
1. a kind of method of determining Self-cooling oil-immersed transformer thermal driving force, which comprises the steps of:
The first step, the oil stream density p for obtaining Self-cooling oil-immersed transformeroil, oil stream coefficient of expansion βoil, Cool Hot Core high potential difference Δ
h；
Second step utilizes thermal driving force Δ pdComputation model calculates the oil stream heat in Self-cooling oil-immersed transformer closed circuit and drives
Power, model are as follows:
Δpd=ρoilgβoilΔhΔθ
In formula, g is acceleration of gravity, and Δ θ is that top-oil temperature and the oil temperature of bottom oil temperature are poor；
The oil temperature difference θ is by monitoring Transformer load current and environment temperature, in conjunction with oil temperature difference prediction model meter
It obtains；
The acquisition pattern of the oil temperature difference prediction model includes the following steps:
(1) top-oil temperature, the bottom oil temperature, environment temperature of the oil-immersed transformer actual measurement of an installing optical fiber temperature-measurement equipment are obtained
Degree, load current data, further subtract bottom oil temperature for top-oil temperature and obtain oil temperature difference data；
(2) basic framework for setting oil-immersed transformer oil temperature difference prediction model is as follows:
In formula, IpuFor load factor, θambIndicate environment temperature, t indicates time variable；
(3) differential value of the oil temperature difference data obtained in (1) in time is calculatedAnd by load current than upper rated current
Obtain load factor Ipu(k), acquired ambient temperature data θamb(k) constant, wherein k indicates discrete-time variable, i.e. gained
Data are the obtained one group of discrete value of sampling, further by oil temperature difference differential value, oil temperature is poor, load factor and ambient temperature data
It is divided into training set and forecast set；
(4) driving modeling is carried out to training set data using genetic programming algorithm, establishes explicit oil temperature difference prediction model, specifically
It is as follows:
1) initialization population: setting genetic programming algorithm operational parameter control, being generated at random by algorithm has Z function individual
Population primary；
The control parameter of algorithm includes the function individual amount Z of population, training algebra G, meets genetic programming termination rules institute
If the maximum node number N of threshold gamma, function individualm, fitness function weight coefficient α1And α2, crossover probability PcAnd mutation probability Pm's
Initial value, collection of functions, leaf node；
2) function individual adaptation degree size is calculated by fitness function based on training set data, fitness value is smaller, function
Body is more excellent；
The fitness function for calculating function individual adaptation degree size is provided that
In formula, Jg,iIt is g for the calculated value of i-th of function individual in population, R (k) is the oil temperature difference differential value in training set, M
For training set size, i.e. the data group number of training set, N is the number of nodes of function individual, εmaxIt is calculated most for current function individual
Big mean absolute error, α1And α2For fitness function weight coefficient；
3) it selects: the individual of the function for carrying out genetic manipulation is selected by roulette method；
4) genetic manipulation is executed to the function individual selected, generates population of lower generation；
The genetic manipulation includes the intersection and variation of function individual, wherein crossover probability Pc, mutation probability PmIt will change with evolution
Generation constantly adaptive change, function individual adaptation degree is smaller, and assigned intersection, mutation probability are bigger；
5) above is repeated 2) to the 4) step, until meeting algorithm termination rules；
The algorithm termination rules, specifically:
1. the difference of adjacent generations maximum adaptation angle value reaches preset threshold gamma, it may be assumed that
|Fmax(Jg,i+1)-Fmax(Jg,i)|≤γ
F in formulamax(Jg,i+1) and Fmax(Jg,i) be respectively adjacent generations maximum adaptation angle value；
2. evolving to predetermined trained algebra G；
Meeting the above wherein rule is the modeling operational process for terminating genetic programming algorithm；
6) using the smallest function individual of fitness value in last generation of genetic programming algorithm as oil temperature difference prediction model；
(5) load factor, environment temperature in forecast set are inputted into oil temperature difference prediction model, obtains the predicted value of oil temperature difference, and benefit
Mean absolute error MAE, the average relative error MRE of oil temperature difference measured value in oil temperature difference predicted value and forecast set are calculated with following formula
And goodness of fit R2, verify model prediction accuracy and accuracy；
In formula, n is data points, fiFor the oil temperature difference predicted value obtained by prediction model, yiIt is real for the oil temperature difference in forecast set
Measured value,For the average value of oil temperature difference in forecast set；
(6) it if the precision of prediction for the model that forecast set is verified in (5) is up to standard, selectes the model and is predicted as final oil temperature difference
Model；If its precision of prediction is not up to standard, (4), (5) are repeated, predict mould until selecting precision of prediction final oil temperature difference up to standard
Type；
The criterion whether precision of prediction is up to standard is specifically set are as follows: if mean absolute error MAE less than 2 DEG C, average phase
To error MRE less than 1.20% and goodness of fit R2Greater than 0.9, then precision of prediction is up to standard, and it is not up to standard to be otherwise considered as precision of prediction.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111814302A (en) * | 2020-05-28 | 2020-10-23 | 嘉兴市恒光电力建设有限责任公司 | Fault removing system and method for transformer insulating oil temperature acquisition and transmission loop |
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Application publication date: 20190416 |
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