CN115730256A - Transformer load capacity increasing strategy based on oil temperature self-adaptive control - Google Patents
Transformer load capacity increasing strategy based on oil temperature self-adaptive control Download PDFInfo
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Abstract
The invention discloses a transformer load capacity increasing strategy based on oil temperature self-adaptive control, belongs to the technical field of power transmission and transformation, and aims to solve the problems that the short-term strategy of the transformer capacity increasing can influence the service life of a transformer and simultaneously has faults and potential safety hazards. The method comprises the steps of preparing test equipment, acquiring various data of the transformer under two working conditions, estimating the hot point temperature of the transformer under the two working conditions, carrying out a self-adaptive control strategy of the temperature of the transformer under the forced oil circulation air cooling working condition based on the Bayesian decision theory, comparing the service lives of the transformer under the two working conditions, constructing a dynamic capacity increasing strategy of the transformer load with multi-source data fusion, and guiding capacity increasing scheduling of the transformer under the actual working condition. The invention actively prolongs the service life of the transformer by improving the working efficiency of the transformer cooling device, can ensure the economic benefit of power enterprises in engineering application and has good popularization prospect.
Description
Technical Field
The invention belongs to the technical field of power transmission and transformation, and particularly relates to a transformer load capacity increasing strategy based on oil temperature self-adaptive control.
Background
With the rapid development of new energy industry in China, large-scale distributed new energy grid connection is realized; in recent years, distributed wind power generation and photovoltaic power generation built on idle land of farmers and roofs of agricultural houses are self-operated, consumed on the spot and surf the internet by surplus electricity. This results in an increasing load level of the main substation on the power supply side; on the power distribution side, the load flow passing through the main transformer is changed from the traditional one-way mode to the two-way mode, the load rate is changed from the traditional single power distribution load curve to the composite load combining the power distribution load and the new energy internet access load, and the load rate of the main transformer is obviously increased.
The large introduction of large-scale distributed new energy causes huge threat to the safe operation of a power grid, if the dynamic capacity increase of the transformer load capacity of a new energy station can be realized, the transformer load can be dynamically regulated and controlled in real time, the potential of the transformer load can be fully exploited, meanwhile, the fault caused by overload can be avoided, and the method has great practical value to the operation and maintenance management of the transformer.
At present, the intelligent key technology research of the transformer mainly covers aspects such as a state evaluation model, an overload model, an insulation aging model, a heat balance model, a cooling model and a fault classification model, and is mostly concentrated on aspects such as the state evaluation model, the insulation aging model, the fault classification model and a communication model, and relatively few researches and engineering applications are performed on aspects such as the overload model, the heat balance model and the cooling model which affect the load energy of the transformer and the economic operation level.
Therefore, the existing power transformer short-term emergency load plan of a power grid company lacks a method for actively regulating and controlling the internal temperature of a distribution transformer, and does not have a related strategy beneficial to dynamic capacity increase of the transformer load, the internal oil temperature of the transformer is correspondingly increased when the short-term emergency load plan runs, the capacity increase is achieved based on the sacrifice of the service life of the transformer, and in the running under the actual working condition, research and development staff are urgently required to solve the problems of economic loss caused by the reduction of the service life of the transformer and faults and potential safety hazards caused by the increase of the short-term load.
Based on the problems in the technical background, research and development personnel provide a transformer load capacity increasing strategy based on oil temperature self-adaptive control.
Disclosure of Invention
The invention aims to provide a transformer load capacity increasing strategy based on oil temperature self-adaptive control, and aims to solve the problems that the short-term strategy of transformer capacity increasing influences the service life of a transformer and has faults and potential safety hazards.
In order to solve the problems, the technical scheme of the invention is as follows:
a transformer load capacity increasing strategy based on oil temperature self-adaptive control comprises the following steps:
s1, preparing test equipment;
s1.1, preparing a transformer under a forced oil circulation air cooling working condition;
the test apparatus comprises: the system comprises a thermocouple (1), a fluorescent optical fiber temperature measuring probe (2), a cooling fan (3), a circulating oil pump (4), an alternating current load box (5), a fluorescent optical fiber demodulator (6), a multi-path temperature tester (7), an intelligent temperature control box (8), an upper computer of an immersion heater (11) and a transformer, wherein the transformer is provided with two groups of oil inlets (9) and oil outlets (10);
1. equipment debugging:
before installation, the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2) are respectively tested, placed in water, tested for sensitivity, and qualified to enter an installation link;
2. equipment installation:
the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2) are arranged at different positions in the transformer and are used for measuring the temperature in the transformer;
the thermocouple (1) is connected with a multi-channel temperature tester (7) through a circuit; the fluorescent optical fiber temperature measuring probe (2) is connected with a fluorescent optical fiber demodulator (6) through a circuit; the fluorescent optical fiber demodulator (6) and the multi-path temperature tester (7) are connected with the upper computer through a circuit;
the heat dissipation fans (3) are arranged on two sides of the heat dissipation fins of the transformer in groups; the circulating oil pumps (4) are in a group and are connected with corresponding oil inlets (9) and oil outlets (10); the cooling fan (3) and the circulating oil pump (4) form a cooling device;
the AC load box (5) is connected with a high-voltage side terminal of the transformer;
an immersion heater (11) is installed inside the transformer;
the temperature intelligent control box (8) is respectively connected with a group of cooling fans (3), a group of circulating oil pumps (4) and a thermocouple (1);
s1.2, preparing a transformer under a natural air cooling working condition;
as shown in fig. 2: the test apparatus includes: the device comprises a thermocouple (1), an alternating current load box (5), a multi-path temperature tester (7), an upper computer of an immersion heater (11) and a transformer;
the thermocouple (1) is connected with a multi-channel temperature tester (7) through a circuit; the multi-channel temperature tester (7) is connected with the upper computer through a circuit;
an AC load box (5) is connected to a high-voltage side terminal of the transformer;
an immersion heater (11) is installed inside the transformer;
s2, acquiring various data of the transformer under two working conditions;
aiming at the transformer which can provide the forced oil circulation air cooling working condition in the S1.1 and the transformer under the natural air cooling working condition in the S1.2, the following numerical values are acquired:
s2.1, acquiring thermocouple data;
after the transformer works for 20min and is stable, measuring the temperature of all corresponding positions in the transformer by using the thermocouple (1), and uploading the measured temperature to an upper computer by using a multi-path temperature tester (7) to form a temperature change curve;
meanwhile, the working states of the cooling fan (3) and the circulating oil pump (4) are controlled through the connection of the intelligent temperature control box (8) and the thermocouple (1);
s2.2, collecting the temperature of the fluorescent optical fiber;
the fluorescent optical fiber temperature measuring probe (2) measures the winding temperature, and the measurement result is uploaded to an upper computer through a fluorescent optical fiber demodulator (6);
s2.3, collecting load current;
the alternating current load box (5) measures the real-time load current of the transformer;
s2.4, a load resistance value;
measuring the load resistance value of the transformer through an alternating current load box (5);
s2.5, collecting the ambient temperature;
obtaining room temperature by a temperature measuring instrument in a room test environment, the meter being theta A ；
S3, estimating the temperature of the hot spot of the transformer under two working conditions;
and aiming at the two groups of values of the transformer capable of providing the forced oil circulation air cooling working condition and the transformer under the natural air cooling working condition collected in the S2, estimating the hot spot temperature of the transformer by adopting the following methods:
transformer hot spot temperature theta H Taking the measured value theta of the hottest point temperature of the winding H1 And calculated value theta of the hottest point temperature of the winding H2 Average value of (c):
θ H ＝(θ H1 +θ H2 )/2 (4)
s4, a transformer temperature self-adaptive control strategy under a forced oil circulation air cooling working condition based on a Bayesian decision theory;
the cooling device control strategy collects the oil temperature of the top layer through the thermocouple (1), and the intelligent temperature control box (8) controls the start and stop of the cooling device;
screening an optimal control strategy by using a Bayesian decision method, balancing the input amount of a cooling device according to the statistical analysis of historical data of the operation of the cooling device, determining the sequence of cooling, and realizing the optimal control strategy of the cooling device;
s5, comparing the service lives of the transformers under the two working conditions;
and (3) aiming at the data of the transformer under the natural air cooling working condition and the data of the transformer under the forced oil circulation air cooling working condition under the control strategy in the S3, estimating the service life of the transformer by adopting the following methods:
s5.1, insulating life of the transformer;
when the load is different, the insulation life of the transformer is directly obtained through the polymerization degree of the insulation oilpaper;
s5.2, simulating the residual service life of the transformer;
when different loads are carried out, modeling is carried out on the test working condition by using the concepts of the state space matrix and the observation matrix, and then the residual service life is calculated;
s5.3, comparing the service lives of the transformers under the two working conditions;
on the basis of the self-adaptive control strategy of the temperature of the transformer under the forced oil circulation air cooling working condition in the S4 step, the service lives of the transformer under the forced oil circulation air cooling working condition and the transformer under the natural air cooling condition are respectively compared, and the effect of the self-adaptive control strategy of the temperature in the S4 step is verified and used for guiding the capacity increasing strategy of the transformer under the actual working condition in the S7 step;
s6, constructing a dynamic capacity increasing strategy of the transformer load of multi-source data fusion;
on the basis of the adaptive control strategy of the transformer temperature in the S4, the internal temperature of the transformer is actively controlled, the dynamic regulation and control of the transformer load are realized, and the dynamic capacity increasing strategy of the transformer load with multi-source data fusion is constructed;
assuming that the prediction deviation value obeys normal distribution N (0, 1) and the sampling values of the environmental temperature and the load rate obey binary normal distribution, the generation method for obtaining the operation condition at a certain moment comprises the following steps:
in formulas (10) and (11):
θ a(i) and K (i) Respectively obtaining the environment temperature and the load rate sampling value at the moment i;
x and Y are random variables (other distributions are possible as the case may be) independent of each other and subject to a normal distribution N (0, 1);
the risk concept can comprehensively reflect the probability of various possible events during the short-time capacity increase of the transformer and the consequences thereof, and the risk concept is defined as the following formula (12):
in the formula:
E i is the ith event that may occur;
pr represents the occurrence probability;
s is at
Consequences caused by Ei under working conditions;
the method comprises the steps that the life loss risk, the failure outage risk and the electricity charge loss risk in the capacity increasing period of the transformer are defined, and the comprehensive capacity increasing risk of the transformer is obtained through fusion;
s7, guiding the capacity-increasing dispatching of the transformer under the actual working condition;
in the actual working condition, based on the comparison of the service lives of the transformers under the two working conditions in the step S5, the comprehensive capacity increasing risk obtained after the service life loss risk, the failure outage risk and the electricity charge loss risk are comprehensively considered in the step S6, and capacity increasing scheduling of the transformers under the actual working condition is determined.
Further, the optimal control strategy screened by the bayesian decision method in S4 specifically comprises:
determining the sequence of the cooling device investment, and balancing the investment amount of the cooling device;
first, the number of events c is fixed, and the state of each event is knownI.e. omega i (i =1, \8230;, c) is known;
second, the probability of each event occurring, i.e., the prior probability P (ω) i ) (i =1, \8230;, c), and knowing the conditional probability density P (x | ω;, c) i ) (i =1, \8230;, c), to solve the problem of how to make the most scientific and reasonable decision on the observed value x;
assume that the amount of time c =2, ω 1 =0 denotes the cooling device off state, ω 2 =1 represents the cooling device operating state;
the prior probability P (omega) of the stop state and the running state of the cooling device is obtained by applying the running experience of the cooling device 1 )、P(ω 2 ) And satisfies P (ω) 1 )+P(ω 2 )＝1；
At known cooling device state ω j On the premise that (j =1,2), the probability of x occurrence is P (x | ω) j ) And obtaining posterior probability of the stop state and the running state of the cooling device by the formula (5);
therefore, the decision process of the top oil temperature is as follows:
1. three characteristic attributes for selecting top oil temperature are a 1 、a 2 、a 3 The method is divided according to the following principle:
a 1 : { the top oil temperature is less than or equal to 45 deg.C };
a 2 : {45 ℃ and < top oil temperature < 55 ℃;
a 3 : { the top oil temperature is more than or equal to 55 ℃;
c =0, and the fan is set to a fan stop state;
c =1, and the state is set as a fan starting state;
2. obtaining a training sample:
selecting top oil temperature data between 45 ℃ and 60 ℃ as training samples, and calculating prior probability of each category in the training samples to obtain P (C = 0) and P (C = 1);
calculating individual tokens under each class conditionA priori probability of attribute partition, P (a) 1 |C＝0)、P(a 2 |C＝0)、P(a 3 |C＝0)、P(a 1 |C＝1)、P(a 2 |C＝1)、P(a 3 |C＝1)；
3. And (3) classifier decision making:
on the basis of obtaining the prior probability through training, a classifier is applied to decide starting and stopping strategies of the cooling device under different top oil temperatures, and the posterior probability is calculated through the classifier; finally, a Bayesian decision result with the hot spot temperature, the load current and the top oil temperature as input quantities is obtained, so that an intelligent cooling control strategy comprehensively considering various factors is realized.
Further, a specific method for simulating the remaining service life of the transformer in S5.2 is as follows:
1. establishing a state equation by using a linear random differential equation:
X K ＝A K X K-1 +B K U K +W K (6)
in formula (6):
X k the remaining service life of the transformer;
U K a system control quantity;
A K 、B K is a system parameter;
X K-1 the system state at the moment k-1;
W K is process noise;
2. let K observe variable Z K Observed noise is V K And obtaining an observation equation:
Z K ＝H K X K +V K (7)
in formula (7):
Z K the observed value at the K moment is obtained;
H K to measure system parameters;
V K to measure noise;
3. with F k Indicating the current remaining service life X of the transformer k And the previous time state X k-1 Non-linear relationship therebetween, the rest of the transformerThe remaining service life can be expressed by a state equation based on a time evolution sequence:
X k ＝F k (X k-1 V k ) (8)
4. in summary, X represents the remaining useful life of the transformer k The measurement can be estimated recursively as shown in equation (9):
Z k ＝H k (X k ,U k ) (9)
therefore, the residual service life of the transformer under the test working condition is simulated by combining the space state equations of the equation (8) and the equation (9) and based on a Kalman filtering algorithm.
Further, the specific steps of obtaining the comprehensive capacity-increasing risk of the transformer by fusing in the S6 are as follows:
s6.1 risk of life loss;
according to Arrhenius theory, the accelerated factor FAA of the insulation life of the transformer and the loss S of the life of the transformer in shorter time dt ll As shown in formulas (13) and (14);
in formulae (13) and (14):
L 0 the rated service life of the transformer;
θ H the temperature of the hottest point of the transformer winding is obtained;
θ 0 is the reference hotspot temperature;
s6.2, failure shutdown risk;
the increase of the load rate directly leads to the increase of the fault probability of the transformer, and the shutdown model of the transformer can adopt an Arrhenius-Weibull model, and the fault rate lambda and the fault probability Pr of the Arrhenius-Weibull model outage As shown in formulas (15) and (16);
in formulas (15) and (16):
beta is a shape parameter;
C. b is an empirical constant;
θ H the hottest point temperature of the transformer winding;
θ 0 is the reference hotspot temperature;
t e and dt e Respectively representing equivalent service time and equivalent shorter time for continuing operation;
s6.3, risk of electric charge loss;
after a certain element in the system is shut down, the element cannot be put into operation before the first-aid repair or overhaul is finished, so that the electricity charge loss of a power enterprise is caused;
the short-term capacity increase of the transformer is to transfer the load of fault equipment or elements needing to be overhauled; after the transformer is shut down at the time i, the maintenance time of the transformer is assumed to be T m Then its electricity charge is lost S ebl Is represented by formula (17):
in formula (17):
N m the number of shorter run times Δ t in the down time period;
K (j) is the load factor at time j;
S N rated capacity for the transformer;
phi is a power factor angle;
P out selling price for the electricity fee;
P in the price of power is charged for power plant;
s6.4, integrating risks;
the 3 types of risks directly concern the economic benefits of power enterprises, and can be applied to the evaluation of power capacity-increasing benefits;
according to the risk concept, the fusion method for integrating the risks of compatibilization is shown as formula (18):
in formula (18):
R isk risk for comprehensive compatibilization;
S total is the comprehensive loss;
N T the number of short times Δ t in the capacity increasing period;
θ H(i) is the hot spot temperature at time i of compatibilization;
pr is the failure probability;
S ll loss of life for the transformer;
S fo shutdown losses for failure;
seb is the electricity charge loss.
Further, the temperature θ of the hot spot of the transformer in S4 H Taking the measured value theta of the hottest point temperature of the winding H1 And calculated value theta of the hottest point temperature of the winding H2 The specific sources of (A) are as follows: the obtained winding temperature is obtained through the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2), wherein the highest temperature value is the measured value theta of the temperature of the hottest point of the winding H1 ；
Directly obtaining top oil temperature counted as a transformer through a thermocouple (1);
calculating the hot spot temperature inside the transformer by the international electrotechnical commission and the national standard, wherein the calculation formula is as follows:
θ H2 ＝θ A +Δθ TO/A +Δθ H/TO (1)
in formula (1):
θ H2 calculating the temperature of the hottest point of the transformer winding;
θ A is ambient temperature;
θ TO/A the difference between the top oil temperature of the transformer and the ambient temperature;
θ H/TO the temperature difference between the hottest point temperature of the winding and the top layer oil is calculated;
in the formula (1), theta TO/A And theta H/TO The calculation method of (c) is as follows:
Δθ H/TO ＝Δθ H,R ×K 2m (3)
in formulas (2) and (3):
θ TO,R the difference between the top oil temperature and the ambient temperature under the rated load condition;
gamma is the ratio of rated load loss to no-load loss;
k is a load coefficient;
m and n are coefficients related to the cooling mode of the transformer, respectively;
θ H,R the difference between the hottest point temperature under the rated load and the top oil temperature is shown.
Further, the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2) are arranged in the manner of S1.1: 2 thermocouples (1) are laid on the top layer of the transformer insulating oil in advance; 10 thermocouples (1) and 3 fluorescent optical fiber temperature measuring probes (2) are laid in the high-voltage and low-voltage windings at the same time;
s1.2 the thermocouple (1) is installed in the following way: 2 transformer insulating oil top layers are installed, and 10 high-voltage and low-voltage windings are laid.
Further, the cooling device in S4 is started in four stages, wherein the starting temperature is 55 ℃, 60 ℃, 65 ℃ and 70 ℃, and the stopping temperature is 45 ℃, 50 ℃, 55 ℃ and 65 ℃.
The invention has the following beneficial effects:
(1) The invention carries out comparison test on the forced oil circulation air-cooled transformer and the natural air-cooled transformer, collects the internal measured temperature data of the two transformers under the same environmental temperature and the same load condition, constructs a temperature self-adaptive control strategy through a Bayes decision theory, collects the insulation life of the two transformers under the control of the strategy, simulates the residual life, and obtains the comprehensive risk under the constraint of the comprehensive constraint condition to evaluate the dynamic load of the transformers by combining the life loss risk under the constraint condition of the temperature, the failure shutdown risk under the constraint condition of the fault rate and the power cost loss risk under the constraint of the economy. And formulating a capacity increasing plan of the transformer load according to the actual working condition, and flexibly adjusting the decision of the transformer operation scheme so as to guide the transformer operation under the actual working condition and take the economic efficiency and the safety into consideration.
(2) Compared with the existing power transformer short-term emergency load plan of a power grid company, the power transformer short-term emergency load plan has better flexibility, and the corresponding economic capacity-increasing service life and the economic capacity-increasing plan duration are obtained by exploring the coupling relation among the transformer service time, the planned capacity-increasing time and the capacity-increasing decision, so that reference is provided for quickly selecting the capacity-increasing transformer in capacity-increasing dispatching. Through the work efficiency who promotes transformer cooling device, make transformer life-span initiative extension, can guarantee electric power enterprise's economic benefits in the engineering application, popularization prospect is good.
Drawings
FIG. 1 is a schematic diagram of a transformer capable of providing forced oil circulation air cooling in embodiment 1;
FIG. 2 is a schematic diagram of a transformer preparation structure in the case of natural air cooling in embodiment 1;
fig. 3 is a flowchart of a strategy for adaptive control of the temperature of the transformer in step S4 in embodiment 1;
FIG. 4 is a flow chart of the present invention.
The reference numbers are as follows: 1-a thermocouple; 2-fluorescent optical fiber temperature measuring probe; 3-a heat dissipation fan; 4-a circulating oil pump; 5-alternating current load box; 6-fluorescent fiber demodulator; 7-a multichannel temperature tester; 8-temperature intelligent control box; 9-an oil inlet; 10-an oil outlet; 11-immersion heater.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive efforts based on the embodiments of the present invention, are within the scope of protection of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention.
Example 1
As shown in fig. 4, a transformer load capacity increase strategy based on adaptive control of oil temperature includes the following steps:
s1, preparing test equipment;
s1.1, preparing a transformer under a forced oil circulation air cooling working condition;
as shown in fig. 1: the test apparatus comprises: the device comprises a thermocouple (1), a fluorescent optical fiber temperature measuring probe (2), a cooling fan (3), a circulating oil pump (4), an alternating current load box (5), a fluorescent optical fiber demodulator (6), a multi-path temperature tester (7), a temperature intelligent control box (8), an immersion heater (11), an upper computer and a transformer, wherein the transformer is an oil-immersed single-phase core type transformer of 220V/2KV2KVA, and is provided with two groups of oil inlets (9) and oil outlets (10).
1. Equipment debugging:
before installation, the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2) are respectively tested, placed in water, tested for sensitivity, and qualified to enter an installation link.
2. Equipment installation:
as shown in fig. 1: the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2) are arranged at different positions in the transformer and used for measuring the temperature in the transformer.
Specifically, the method comprises the following steps: the thermocouple (1) is a K-type sheathed thermocouple WRNK-191 type, and the diameter of the thermocouple is 1mm; the fluorescent optical fiber temperature measuring probe (2) is of an IF-TF type, the diameter is 3.5mm, the precision is +/-1 ℃, and the temperature measuring range is-40-200 ℃; both set to measure the temperature every two seconds. The installation mode is as follows: 12 thermocouples (1) and 3 fluorescent optical fiber temperature measuring probes (2) are respectively arranged on the high-low voltage winding and the top layer of the transformer insulating oil. Specifically, the method comprises the following steps: 2 thermocouples (1) are laid on the top layer of the transformer insulating oil in advance; 10 thermocouples (1) and 3 fluorescent optical fiber temperature probes (2) are laid in the high-voltage and low-voltage windings at the same time.
The thermocouple (1) is connected with a multi-channel temperature tester (7) through a circuit; the fluorescent optical fiber temperature measuring probe (2) is connected with a fluorescent optical fiber demodulator (6) through a circuit; the fluorescent optical fiber demodulator (6) and the multi-path temperature tester (7) are connected with the upper computer through a circuit; the temperature monitoring device is used for monitoring the variation trend of the temperature inside the transformer in real time and giving an alarm when the temperature exceeds a threshold value.
The heat radiation fans (3) are arranged on two sides of the heat radiation fins of the transformer in groups and are subjected to forced air cooling in the test.
The circulating oil pumps (4) are in a group and are respectively connected with the corresponding oil inlet (9) and the corresponding oil outlet (10) through plastic corrugated pipes with the diameter of 23 mm.
The cooling device is composed of the cooling fan (3) and the circulating oil pump (4), oil in the transformer is circulated by the circulating oil pump (4), and the purpose of reducing the internal temperature of the transformer is achieved by matching the cooling fan (3).
The alternating current load box (5) is a resistive load and is connected with a high-voltage side terminal of the transformer; the control is realized through the PLC, and the control on the load capacity of the transformer and the recording on the load current are realized. In the test, in order to simulate the change condition of a daily load curve, the change of the daily load of the transformer is realized by adjusting the resistance value of the alternating current load box (5), and the internal temperature of the transformer is controlled by the intelligent temperature control box (8).
The immersion heater (11) is installed inside the transformer and used for adjusting the oil temperature inside the transformer, simulating the test condition that the oil temperature inside the transformer is high in the high-temperature environment and starting and stopping manually. And intelligently controlling the cooling device to regulate the internal temperature by combining the Bayesian decision theory.
A set of cooling fan (3), a set of circulating oil pump (4), thermocouple (1) are connected respectively in temperature intelligent control case (8), make things convenient for temperature intelligent control case (8) to gather the data back of thermocouple (1), control the inside temperature of transformer through opening of controlling cooling device for inside top layer oil temperature and winding temperature are no longer than relevant specified value.
S1.2, preparing a transformer under a natural air cooling working condition;
as shown in fig. 2: the test apparatus comprises: the device comprises a thermocouple (1), an alternating current load box (5), a multi-path temperature tester (7), an upper computer of an immersion heater (11) and a transformer, wherein the transformer also adopts a 220V/2KV2KVA oil-immersed single-phase core type transformer.
The specific diameter of the thermocouple (1) is 1mm, the precision is +/-1 ℃, and the temperature is set to be measured every two seconds. The installation mode is as follows: 12 thermocouples (1) are arranged, 2 thermocouples are arranged on the top layer of the insulating oil of the transformer, and 10 thermocouples are laid in the high-voltage and low-voltage windings.
The thermocouple (1) is connected with a multi-channel temperature tester (7) through a circuit; the multi-channel temperature tester (7) is connected with the upper computer through a line.
The AC load box (5) is connected to the high-voltage side terminal of the transformer.
An immersion heater (11) is mounted inside the transformer.
S2, data acquisition under two working conditions;
aiming at the transformer capable of providing a forced oil circulation air cooling working condition in S1.1 and the transformer under a natural air cooling working condition in S1.2, the following numerical values are collected:
s2.1, acquiring thermocouple data;
after the transformer works for 20min and is stable, all corresponding positions (including positions corresponding to the top layer of transformer insulating oil and high and low voltage windings) in the transformer begin to be measured through the thermocouple (1), and the measured temperature is uploaded to an upper computer through a multi-path temperature tester (7) to form a temperature change curve. Meanwhile, the working states of the cooling fan (3) and the circulating oil pump (4) are controlled through the connection of the intelligent temperature control box (8) and the thermocouple (1).
S2.2, collecting the temperature of the fluorescent optical fiber;
the fluorescent optical fiber temperature measuring probe (2) measures the temperature of the winding, and the measurement result is uploaded to an upper computer through a fluorescent optical fiber demodulator (6).
S2.3, collecting load current;
the AC load box (5) measures the real-time load current of the transformer.
S2.4, a load resistance value;
the load resistance value of the transformer is measured through the AC load box (5).
S2.5, ambient temperature acquisition
Obtaining room temperature by a temperature measuring instrument in a room test environment, measured as theta A 。
S3, estimating the temperature of the hot spot of the transformer under two working conditions;
and aiming at the two groups of values of the transformer capable of providing the forced oil circulation air cooling working condition and the transformer under the natural air cooling working condition collected in the S2, estimating the hot spot temperature of the transformer by adopting the following methods:
the obtained winding temperature is obtained through the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2), wherein the highest temperature value is the measured value theta of the temperature of the hottest point of the winding H1 。
The top oil temperature measured as the transformer is directly obtained by the thermocouple (1).
Calculating the temperature of the hot spot inside the transformer through the international electrotechnical commission and the national standard, wherein the calculation formula is as follows:
θ H2 ＝θ A +Δθ TO/A +Δθ H/TO (1)
in formula (1):
θ H2 calculating the temperature of the hottest point of the transformer winding;
θ A is ambient temperature;
θ TO/A the difference between the top oil temperature of the transformer and the ambient temperature;
θ H/TO the temperature difference between the hottest point temperature of the winding and the top layer oil is obtained;
in the formula (1) < theta > TO/A And theta H/TO The calculation of (c) is as follows:
Δθ H/TO ＝Δθ H,R ×K 2m (3)
in formulas (2) and (3):
θ TO,R the difference between the top oil temperature and the ambient temperature under the rated load condition;
gamma is the ratio of rated load loss to no-load loss;
k is a load factor;
m and n are coefficients related to the cooling mode of the transformer, respectively;
θ H,R the difference between the hottest point temperature under the rated load and the top oil temperature is shown.
Transformer hot spot temperature theta H Taking the average value of the measured value of the temperature of the hottest point of the winding and the calculated value of the temperature of the hottest point of the winding:
θ H ＝(θ H1 +θ H2 )/2 (4)
s4, a self-adaptive control strategy of the temperature of the transformer under the forced oil circulation air cooling working condition based on a Bayesian decision theory;
the cooling device control strategy follows the national standard GB/T1094.7-2008 and DL/T572-2010 regulations. The cooling device on-off grading is based on Q/GDW736.4-2012 part 4 of technical condition of intelligent power transformer: the cooling device controls IED technical conditions, and controls the cooling device of the transformer according to the conditions of the top oil temperature start-stop device.
The temperature of the oil at the top layer is collected through a thermocouple (1), and an intelligent temperature control box (8) controls the start and stop of the cooling device. The cooling device is started in four stages, wherein the starting temperature is 55 ℃, 60 ℃, 65 ℃ and 70 ℃, and the stopping temperature is 45 ℃, 50 ℃, 55 ℃ and 65 ℃. Balancing the input amount of the cooling device, determining the sequence of cooling, and realizing the optimal control strategy of the cooling device, as shown in fig. 4.
And (3) applying a Bayesian decision method to the screening of the optimal control strategy, and statistically analyzing the input condition of the device according to the historical data of the operation of the cooling device:
the method comprises the steps of determining the sequence of the cooling device investment and balancing the investment of the cooling device.
First, the number of events, c, is fixed and the state of each event is known, i.e., ω i (i =1, \8230;, c) is known;
second, the probability of each event occurring, i.e., the prior probability P (ω) i ) (i =1, \8230;, c), and knowing the conditional probability density P (x | ω;, c) i ) (i =1, \8230;, c), so as to solve the problem of how to make the most scientific and reasonable decision on the observed value x.
Assume that the number of times c =2, ω 1 =0 denotes the cooling device off state, ω 2 =1 represents the cooling device operating state;
the prior probability P (omega) of the stop state and the running state of the cooling device is obtained by applying the running experience of the cooling device 1 )、P(ω 2 ) And satisfies P (ω) 1 )+P(ω 2 )＝1。
At known cooling device state ω j On the premise that (j =1,2), the probability of x occurrence is P (x | ω) j ) And the posterior probability of the stop state and the operation state of the cooling device is obtained by the formula (5).
Therefore, the decision process of the top oil temperature is as follows:
1. three characteristic attributes of the top oil temperature are selected as a 1 、a 2 、a 3 The method is divided according to the following principle:
a 1 : { the top oil temperature is less than or equal to 45 ℃;
a 2 : {45 ℃ and < top oil temperature < 55 ℃;
a 3 : { the top oil temperature is more than or equal to 55 ℃;
c =0, and a fan stop state is set;
c =1, and the fan is set to the fan start state.
2. Obtaining a training sample:
and selecting top oil temperature data between 45 ℃ and 60 ℃ as training samples, and calculating the prior probability of each category in the training samples to obtain P (C = 0) and P (C = 1).
Calculating the prior probability of each characteristic attribute partition under each category condition, P (a) 1 |C＝0)、P(a 2 |C＝0)、P(a 3 |C＝0)、P(a 1 |C＝1)、P(a 2 |C＝1)、P(a 3 |C＝1)。
3. And (3) classifier decision making:
on the basis of obtaining the prior probability through training, a classifier is applied to decide starting and stopping strategies of the cooling device under different top oil temperatures, and the posterior probability is calculated through the classifier.
According to the thought, the Bayesian decision result with the hot spot temperature, the load current and the top oil temperature as the input quantity can be obtained, and the intelligent cooling control strategy taking various factors into consideration can be realized.
S5, comparing the service lives of the transformers under the two working conditions;
and (4) aiming at the data of the transformer under the natural air cooling working condition collected in the S3 and the data of the transformer under the forced oil circulation air cooling working condition under the control strategy applied in the S3, estimating the service life of the transformer by adopting the following method:
s5.1, the insulation life of the transformer is prolonged;
the insulation life of the transformer is directly obtained by the degree of polymerization of the insulating oil paper at different loads.
S5.2, simulating the residual service life of the transformer;
and when different loads are carried out, modeling is carried out on the test working condition by using the concepts of the state space matrix and the observation matrix, and then the residual service life is calculated.
1. Establishing a state equation by using a linear random differential equation:
X K ＝A K X K-1 +B K U K +W K (6)
in formula (6):
X k the remaining service life of the transformer;
U K a system control quantity;
A K 、B K is a system parameter;
X K-1 the system state at the moment k-1;
W K is process noise.
2. Let K observe variable Z K Observed noise is V K And obtaining an observation equation:
Z K ＝H K X K +V K (7)
in formula (7):
Z K the observed value at the moment K is obtained;
H K to measure system parameters;
V K to measure noise.
3. By F k Representing the current remaining service life X of the transformer k And the previous time state X k-1 The remaining service life of the transformer can be expressed by a state equation based on a time evolution sequence:
X k ＝F k (X k-1 V k ) (8)
4. in summary, X represents the remaining useful life of the transformer k The measurement can be estimated recursively as shown in equation (9):
Z k ＝H k (X k ,U k ) (9)
therefore, the prediction of the residual service life of the transformer can be combined with the space state equation of the formula (8) and the formula (9), and the residual service life of the transformer under the test working condition is simulated based on the Kalman filtering algorithm.
S5.3, comparing the service life of the transformer under the two working conditions;
on the basis of the transformer temperature self-adaptive control strategy under the forced oil circulation air cooling working condition in the S4 step, the service lives of the transformer under the forced oil circulation air cooling working condition and the transformer under the natural air cooling condition are respectively compared, and the effect of the S4 temperature self-adaptive control strategy is verified and used for guiding the transformer capacity increasing strategy under the actual working condition in the S7 step.
S6, constructing a dynamic capacity increasing strategy of the transformer load with multi-source data fusion;
and (4) on the basis of the self-adaptive control strategy of the transformer temperature in the S4, actively controlling the internal temperature of the transformer, realizing dynamic regulation and control of the transformer load, and constructing a dynamic capacity increasing strategy of the transformer load with multi-source data fusion.
Assuming that the prediction deviation value obeys normal distribution N (0, 1) and the sampling values of the environmental temperature and the load rate obey binary normal distribution, the generation method for obtaining the operation condition at a certain moment comprises the following steps:
in formulas (10) and (11):
θ a(i) and K (i) Respectively sampling values of the environment temperature and the load rate at the moment i;
x and Y are random variables (other distributions are possible as the case may be) independent of each other and subject to a normal distribution N (0, 1);
In the capacity increasing process of the transformer, the load rate is increased, the winding temperature is also increased, the insulation aging speed of the transformer is accelerated, and the adverse effects of transformer failure, outage and the like are caused.
The load dynamic capacity increase of the transformer is realized through the service life constraint and the fault constraint of the transformer.
The risk concept can comprehensively reflect the probability of various possible events during the short-time capacity increase of the transformer and the consequences thereof, and the risk concept is defined as the following formula (12):
in the formula:
E i is the ith event that may occur;
pr represents the occurrence probability;
s is at
The consequences of Ei under operating conditions.
The comprehensive capacity increasing risk of the transformer is obtained by defining the life loss risk, the failure outage risk and the electric charge loss risk in the capacity increasing period of the transformer and fusing, and the specific steps are as follows:
s6.1 risk of life loss;
according to Arrhenius theory, the accelerated factor FAA of the insulation life of the transformer and the loss S of the life of the transformer in shorter time dt ll As shown in formulas (13) and (14).
In formulas (13) and (14):
L 0 the rated service life of the transformer;
θ H the temperature of the hottest point of the transformer winding is obtained;
θ 0 is the reference hot spot temperature.
S6.2, failure shutdown risk;
the increase of the load rate directly leads to the increase of the fault probability of the transformer, and the shutdown model of the transformer can adopt an Arrhenius-Weibull model, and the fault rate lambda and the fault probability Pr of the Arrhenius-Weibull model outage As shown in formulas (15) and (16).
In formulas (15), (16):
beta is a shape parameter;
D. b are empirical constants;
θ H the hottest point temperature of the transformer winding;
θ 0 is the reference hotspot temperature;
t e and dt e Respectively representing the equivalent service time and the equivalent shorter time for continuing running.
S6.3, risk of electric charge loss;
after a certain element in the system is shut down, the element cannot be put into operation before the first-aid repair or overhaul is finished, and the electricity charge loss of an electric power enterprise is caused.
The short-term capacity increase of the transformer is realized byWith the load of faulty equipment or elements to be serviced. After the transformer is shut down at the time i, the maintenance time of the transformer is assumed to be T m Then its electricity fee loss S ebl Is of formula (17):
in formula (17):
N m the number of shorter run times Δ t for the outage period;
K (j) the load factor at time j is;
S N rated capacity for the transformer;
phi is a power factor angle;
P out selling price for the electricity fee;
P in the price of power is charged for power station.
S6.4, integrating risks;
the 3 types of risks directly concern the economic benefits of power enterprises, and can be applied to the evaluation of power capacity-increasing benefits.
According to the risk concept, the fusion method for comprehensively increasing the risk is shown as the formula (18):
in formula (18):
R isk risk for comprehensive compatibilization;
S total is the comprehensive loss;
N T the number of short times Δ t in the capacity increasing period;
θ H(i) is the hot spot temperature at the moment i of compatibilization;
pr is the failure probability;
S ll loss of life for the transformer;
S fo shutdown losses for failure;
seb is the electricity charge loss.
S7, guiding the capacity increasing dispatching of the transformer under the actual working condition;
in actual working conditions, based on comparison of the service lives of the transformers under the two working conditions in the step S5, the comprehensive capacity increasing risk obtained after the service life loss risk, the failure outage risk and the electricity charge loss risk are comprehensively considered in the step S6, and capacity increasing scheduling of the transformers under the actual working conditions is determined.
Claims (7)
1. A transformer load capacity-increasing strategy based on oil temperature self-adaptive control is characterized in that: the strategy comprises the following steps:
s1, preparing test equipment;
s1.1, preparing a transformer under a forced oil circulation air cooling working condition;
the test apparatus comprises: the system comprises a thermocouple (1), a fluorescent optical fiber temperature measuring probe (2), a cooling fan (3), a circulating oil pump (4), an alternating current load box (5), a fluorescent optical fiber demodulator (6), a multi-path temperature tester (7), an intelligent temperature control box (8), an upper computer of an immersion heater (11) and a transformer, wherein the transformer is provided with two groups of oil inlets (9) and oil outlets (10);
1. equipment debugging:
before installation, the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2) are respectively tested, placed in water, tested for sensitivity, and qualified to enter an installation link;
2. equipment installation:
the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2) are arranged at different positions in the transformer and are used for measuring the temperature in the transformer;
the thermocouple (1) is connected with a multi-channel temperature tester (7) through a circuit; the fluorescent optical fiber temperature measuring probe (2) is connected with a fluorescent optical fiber demodulator (6) through a circuit; the fluorescent optical fiber demodulator (6) and the multi-path temperature tester (7) are connected with the upper computer through a circuit;
the heat dissipation fans (3) are arranged on two sides of the heat dissipation fins of the transformer in groups; the circulating oil pumps (4) are in a group and are connected with the corresponding oil inlets (9) and oil outlets (10); the cooling fan (3) and the circulating oil pump (4) form a cooling device;
an AC load box (5) is connected to a high-voltage side terminal of the transformer;
an immersion heater (11) is installed inside the transformer;
the temperature intelligent control box (8) is respectively connected with a group of cooling fans (3), a group of circulating oil pumps (4) and a thermocouple (1);
s1.2, preparing a transformer under a natural air cooling working condition;
as shown in fig. 2: the test apparatus comprises: the device comprises a thermocouple (1), an alternating current load box (5), a multi-path temperature tester (7), an upper computer of an immersion heater (11) and a transformer;
the thermocouple (1) is connected with a multi-channel temperature tester (7) through a circuit; the multi-channel temperature tester (7) is connected with the upper computer through a line;
an AC load box (5) is connected to a high-voltage side terminal of the transformer;
an immersion heater (11) is installed inside the transformer;
s2, acquiring various data of the transformer under two working conditions;
aiming at the transformer capable of providing a forced oil circulation air cooling working condition in S1.1 and the transformer under a natural air cooling working condition in S1.2, the following numerical values are collected:
s2.1, acquiring thermocouple data;
after the transformer works for 20min and is stable, measuring the temperature of all corresponding positions in the transformer by using the thermocouple (1), and uploading the measured temperature to an upper computer by using a multi-path temperature tester (7) to form a temperature change curve;
meanwhile, the working states of the cooling fan (3) and the circulating oil pump (4) are controlled through the connection of the intelligent temperature control box (8) and the thermocouple (1);
s2.2, collecting the temperature of the fluorescent optical fiber;
the fluorescent optical fiber temperature measuring probe (2) measures the winding temperature, and the measurement result is uploaded to an upper computer through a fluorescent optical fiber demodulator (6);
s2.3, collecting load current;
the alternating current load box (5) measures the real-time load current of the transformer;
s2.4, a load resistance value;
measuring the load resistance value of the transformer through an alternating current load box (5);
s2.5, collecting the ambient temperature;
obtaining room temperature by a temperature measuring instrument in a room test environment, measured as theta A ；
S3, estimating the temperature of the hot spot of the transformer under two working conditions;
and (3) aiming at two groups of values of the transformer capable of providing the forced oil circulation air cooling working condition and the transformer under the natural air cooling working condition, which are acquired in the step (S2), estimating the hot point temperature of the transformer by adopting the following method:
transformer hot spot temperature theta H Taking the measured value theta of the hottest point temperature of the winding H1 And calculated value theta of the hottest point temperature of the winding H2 Average value of (d):
θ H ＝(θ H1 +θ H2 )/2 (4)
s4, a self-adaptive control strategy of the temperature of the transformer under the forced oil circulation air cooling working condition based on a Bayesian decision theory;
the cooling device control strategy collects the oil temperature of the top layer through a thermocouple (1), and an intelligent temperature control box (8) controls the start and stop of the cooling device;
screening an optimal control strategy by using a Bayesian decision method, balancing the input amount of a cooling device according to the statistical analysis of historical data of the operation of the cooling device, determining the sequence of cooling, and realizing the optimal control strategy of the cooling device;
s5, comparing the service lives of the transformers under the two working conditions;
and (3) aiming at the data of the transformer under the natural air cooling working condition and the data of the transformer under the forced oil circulation air cooling working condition under the control strategy in the S3, estimating the service life of the transformer by adopting the following methods:
s5.1, the insulation life of the transformer is prolonged;
when the load is different, the insulation life of the transformer is directly obtained through the polymerization degree of the insulation oilpaper;
s5.2, simulating the residual service life of the transformer;
when different loads are applied, modeling the test working condition by using the concepts of the state space matrix and the observation matrix, and further calculating the residual life;
s5.3, comparing the service life of the transformer under the two working conditions;
on the basis of a transformer temperature self-adaptive control strategy under the forced oil circulation air cooling working condition in the S4 step, the two service lives of the transformer under the forced oil circulation air cooling working condition and the transformer under the natural air cooling condition are respectively compared, and the effect of the S4 temperature self-adaptive control strategy is verified and used for guiding a transformer capacity increasing strategy under the actual working condition in the S7 step;
s6, constructing a dynamic capacity increasing strategy of the transformer load with multi-source data fusion;
on the basis of the self-adaptive control strategy of the transformer temperature in S4, the internal temperature of the transformer is actively controlled, the dynamic regulation and control of the transformer load are realized, and the dynamic capacity increasing strategy of the transformer load with multi-source data fusion is constructed;
assuming that the prediction deviation value obeys normal distribution N (0, 1) and the sampling values of the environmental temperature and the load rate obey binary normal distribution, the generation method for obtaining the operation condition at a certain moment comprises the following steps:
in formulas (10) and (11):
θ a(i) and K (i) Respectively obtaining the environment temperature and the load rate sampling value at the moment i;
x and Y are random variables (other distributions are possible as the case may be) independent of each other and subject to a normal distribution N (0, 1);
the risk concept can comprehensively reflect the probability of various possible events during the short-time capacity increase of the transformer and the consequences thereof, and the risk concept is defined as the following formula (12):
in the formula:
E i is the ith event that may occur;
pr represents the occurrence probability;
s is at
Consequences caused by Ei under working conditions;
the method comprises the steps that the life loss risk, the failure outage risk and the electricity charge loss risk in the capacity increasing period of the transformer are defined, and the comprehensive capacity increasing risk of the transformer is obtained through fusion;
s7, guiding the capacity-increasing dispatching of the transformer under the actual working condition;
in the actual working condition, based on the comparison of the service lives of the transformers under the two working conditions in the step S5, the comprehensive capacity increasing risk obtained after the service life loss risk, the failure outage risk and the electricity charge loss risk are comprehensively considered in the step S6, and capacity increasing scheduling of the transformers under the actual working condition is determined.
2. The transformer load capacity increase strategy based on adaptive control of oil temperature according to claim 1, characterized in that: the Bayesian decision method for screening the optimal control strategy in S4 specifically comprises the following steps:
determining the sequence of the cooling device investment, and balancing the investment amount of the cooling device;
first, the number of events, c, is fixed and the state of each event is known, i.e., ω i (i =1, \8230;, c) is known;
second, the probability of each event occurring, i.e., the prior probability P (ω) i ) (i =1, \8230;, c), and knowing the conditional probability density P (x | ω;, c) i ) (i =1, \8230;, c), to solve the problem of how to make the most scientific and reasonable decision on the observed value x;
assume that the amount of time c =2, ω 1 =0 represents the cooling device off state, ω 2 =1 represents the cooling device operating state;
the prior probability P (omega) of the stop state and the running state of the cooling device is obtained by applying the running experience of the cooling device 1 )、P(ω 2 ) And satisfies P (ω) 1 )+P(ω 2 )＝1；
At known cooling device state ω j On the premise that (j =1,2), the probability of x occurrence is P (x | ω) j ) And obtaining posterior probability of the stop state and the running state of the cooling device by the formula (5);
therefore, the decision process of the top oil temperature is as follows:
1. three characteristic attributes for selecting top oil temperature are a 1 、a 2 、a 3 The method is divided according to the following principle:
a 1 : { the top oil temperature is less than or equal to 45 ℃;
a 2 : {45 ℃ and < top oil temperature < 55 ℃;
a 3 : { the top oil temperature is more than or equal to 55 ℃;
c =0, and a fan stop state is set;
c =1, and the state is set as a fan starting state;
2. obtaining a training sample:
selecting top layer oil temperature data between 45 ℃ and 60 ℃ as training samples, and calculating prior probability of each category in the training samples to obtain P (C = 0) and P (C = 1);
calculating the prior probability of each characteristic attribute partition under each class condition, P (a) 1 |C＝0)、P(a 2 |C＝0)、P(a 3 |C＝0)、P(a 1 |C＝1)、P(a 2 |C＝1)、P(a 3 |C＝1)；
3. And (3) classifier decision making:
on the basis of obtaining the prior probability through training, a classifier is applied to decide starting and stopping strategies of the cooling device under different top oil temperatures, and the posterior probability is calculated through the classifier; finally, a Bayesian decision result with the hot spot temperature, the load current and the top oil temperature as input quantities is obtained, so that an intelligent cooling control strategy comprehensively considering various factors is realized.
3. The transformer load capacity increase strategy based on adaptive control of oil temperature according to claim 1, characterized in that: the specific method for simulating the residual service life of the transformer in S5.2 is as follows:
1. establishing a state equation by using a linear random differential equation:
X K ＝A K X K-1 +B K U K +W K (6)
in formula (6):
X k the remaining service life of the transformer;
U K a system control quantity;
A K 、B K is a system parameter;
X K-1 the system state at the moment k-1;
W K is process noise;
2. let K observe variable Z K Observed noise is V K And obtaining an observation equation:
Z K ＝H K X K +V K (7)
in formula (7):
Z K the observed value at the moment K is obtained;
H K to measure system parameters;
V K to measure noise;
3. by F k Representing the current remaining service life X of the transformer k And the previous time state X k-1 The remaining service life of the transformer can be expressed by a state equation based on a time evolution sequence:
X k ＝F k (X k-1 V k ) (8)
4. in summary, X represents the remaining useful life of the transformer k The measurement can be recursively estimated as shown in equation (9):
Z k ＝H k (X k ,U k ) (9)
therefore, the residual service life of the transformer under the test working condition is simulated by combining the space state equations of the formula (8) and the formula (9) based on the Kalman filtering algorithm.
4. The transformer load capacity increase strategy based on adaptive control of oil temperature according to claim 1, characterized in that: the specific steps for obtaining the comprehensive capacity-increasing risk of the transformer by fusion in the S6 are as follows:
s6.1 risk of loss of life;
according to the Arrhenius theory, the acceleration factor FAA of the insulation life of the transformer and the loss S of the life of the transformer in a shorter time dt ll As shown in formulas (13) and (14);
in formulas (13) and (14):
L 0 the rated service life of the transformer;
θ H the temperature of the hottest point of the transformer winding is obtained;
θ 0 is the reference hotspot temperature;
s6.2, failure shutdown risk;
the increase of the load rate directly leads to the increase of the fault probability of the transformer, and the shutdown model of the transformer can adopt an Arrhenius-Weibull model, and the fault rate lambda and the fault probability Pr of the Arrhenius-Weibull model outage As shown in formulas (15) and (16);
in formulas (15) and (16):
beta is a shape parameter;
C. b are empirical constants;
θ H the hottest point temperature of the transformer winding;
θ 0 is the reference hotspot temperature;
t e and dt e Respectively representing equivalent service time and equivalent shorter time for continuing operation;
s6.3, risk of electric charge loss;
after a certain element in the system is shut down, the element cannot be put into operation before the first-aid repair or overhaul is finished, so that the electricity charge loss of an electric power enterprise is caused;
the short-term capacity increase of the transformer is to transfer the load of fault equipment or elements needing to be overhauled; after the transformer is shut down at the time i, the maintenance time of the transformer is assumed to be T m Then its electricity fee loss S ebl Is of formula (17):
in formula (17):
N m the number of shorter run times Δ t for the outage period;
K (j) is the load factor at time j;
S N rated capacity for the transformer;
phi is a power factor angle;
P out selling price for the electricity fee;
P in the price of the power is on line for the power plant;
s6.4, integrating risks;
the 3 types of risks directly concern the economic benefits and the economic benefits of power enterprises and can be applied to power capacity-increasing benefit evaluation;
according to the risk concept, the fusion method for comprehensively increasing the risk is shown as the formula (18):
in formula (18):
R isk risk for comprehensive compatibilization;
S total the comprehensive loss is realized;
N T the number of short times Δ t in the capacity increasing period;
θ H(i) is the hot spot temperature at time i of compatibilization;
pr is the failure probability;
S ll loss of life for the transformer;
S fo loss of outage for failure;
seb is the electricity charge loss.
5. The transformer load capacity increase strategy based on adaptive control of oil temperature according to claim 1, characterized in that: s4, the temperature theta of the hot spot of the transformer H Taking the measured value theta of the hottest point temperature of the winding H1 And calculated value theta of the hottest point temperature of the winding H2 The specific sources of (A) are as follows: the obtained winding temperature is obtained through the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2), wherein the highest temperature value is the measured value theta of the winding hottest point temperature H1 ；
Directly obtaining top oil temperature counted as a transformer through a thermocouple (1);
calculating the hot spot temperature inside the transformer by the international electrotechnical commission and the national standard, wherein the calculation formula is as follows:
θ H2 ＝θ A +Δθ TO/A +Δθ H/TO (1)
in formula (1):
θ H2 calculating the temperature of the hottest point of the transformer winding;
θ A is ambient temperature;
θ TO/A the difference between the top oil temperature of the transformer and the ambient temperature;
θ H/TO the temperature difference between the hottest point temperature of the winding and the top layer oil is obtained;
in the formula (1) < theta > TO/A And theta H/TO The calculation of (c) is as follows:
Δθ H/TO ＝Δθ H,R ×K 2m (3)
in formulas (2) and (3):
θ TO,R the difference between the top oil temperature and the ambient temperature under the rated load condition;
gamma is the ratio of rated load loss to no-load loss;
k is a load factor;
m and n are coefficients related to the cooling mode of the transformer, respectively;
θ H,R the difference between the hottest point temperature under the rated load and the top oil temperature is shown.
6. The transformer load capacity increase strategy based on adaptive control of oil temperature according to claim 1, characterized in that: s1.1, the thermocouple (1) and the fluorescent optical fiber temperature measuring probe (2) are installed in the following modes: 2 thermocouples (1) are laid on the top layer of the transformer insulating oil in advance; 10 thermocouples (1) and 3 fluorescent optical fiber temperature probes (2) are laid in the high-voltage and low-voltage windings at the same time;
s1.2 the thermocouple (1) is installed in the following way: 2 transformer insulating oil top layers are installed, and 10 high-voltage and low-voltage windings are laid.
7. The transformer load capacity increase strategy based on adaptive control of oil temperature according to claim 1, characterized in that: and S4, starting the cooling device in four stages, wherein the starting temperature is 55 ℃, 60 ℃, 65 ℃ and 70 ℃, and the stopping temperature is 45 ℃, 50 ℃, 55 ℃ and 65 ℃.
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| CN117435890A (en) * | 2023-12-20 | 2024-01-23 | 深圳市武迪电子科技有限公司 | Multi-mode fusion thermal management method and system for electric motorcycle |
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| CN116499531A (en) * | 2023-06-27 | 2023-07-28 | 中能国研（北京）电力科学研究院 | Power equipment state evaluation method and device, electronic equipment and storage medium |
| CN116499531B (en) * | 2023-06-27 | 2023-09-01 | 中能国研（北京）电力科学研究院 | Power equipment state evaluation method and device, electronic equipment and storage medium |
| CN117435890A (en) * | 2023-12-20 | 2024-01-23 | 深圳市武迪电子科技有限公司 | Multi-mode fusion thermal management method and system for electric motorcycle |
| CN117435890B (en) * | 2023-12-20 | 2024-04-02 | 深圳市武迪电子科技有限公司 | Multi-mode fusion thermal management method and system for electric motorcycle |
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