CN116451597A - Method and system for optimizing high-voltage indoor temperature control of transformer substation - Google Patents
Method and system for optimizing high-voltage indoor temperature control of transformer substation Download PDFInfo
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- 230000017525 heat dissipation Effects 0.000 claims abstract description 86
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 26
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 17
- 230000002068 genetic effect Effects 0.000 claims abstract description 17
- 230000008859 change Effects 0.000 claims description 73
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- 238000005457 optimization Methods 0.000 claims description 7
- 230000020169 heat generation Effects 0.000 claims description 6
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- 230000000694 effects Effects 0.000 description 5
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention provides a method and a system for optimizing the temperature control in a high-voltage room of a transformer substation, comprising the steps of collecting temperature data of different operation stages of indoor equipment of the transformer substation; taking the acquired temperature data as initial boundary temperature data, and establishing an indoor heat dissipation model, wherein the indoor heat dissipation model comprises the relation between the output of a heat dissipation device and the target temperature of indoor equipment; determining the layout of the indoor heat dissipation device by adopting a genetic algorithm, initializing a population, wherein each individual in the population is a gene string which represents one layout of the heat dissipation device, and constructing an fitness function according to the output and the target temperature of the heat dissipation device; and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached. According to the invention, the temperature data of the high-pressure indoor equipment in different stages is obtained and is used as an initial boundary temperature, a heat dissipation model is constructed, and the layout of the heat dissipation device is determined by utilizing a genetic algorithm, so that a high-pressure room temperature control scheme with higher efficiency can be obtained.
Description
Technical Field
The invention belongs to the technical field of temperature control of high-voltage chambers of substations, and particularly relates to a temperature control optimization method and system in a high-voltage chamber of a substation.
Background
At present, the number of indoor substations gradually becomes more and more, the indoor substation equipment is compact, the occupied area is small, but the indoor environment heat dissipation effect is poor. In order to ensure safe operation of the equipment, auxiliary heat dissipation tools such as an air conditioner or a fan are often arranged in the equipment room.
The temperature control of the indoor equipment in the transformer substation is mainly realized by radiating through an air conditioner or a fan, but the air conditioner and the fan are basically arranged at fixed positions, and if the indoor space is large, the indoor temperature difference can be obviously sensed. In addition, the temperature control technology used by some intelligent stations is also realized by collecting the temperature, starting the equipment, reducing the temperature and stopping after the return difference value is reached. However, the same problem is that the temperature measuring points are fixed, and the final adjusting effect is not very good. In addition, the simpler method is to put into the heat dissipating device for a long time, the economy is not high, the station electricity consumption proportion is high, and in addition, the effect cannot be accurately judged.
Disclosure of Invention
In view of the above, the invention aims to solve the problem that the temperature control heat dissipation method adopted by the existing transformer substation high-pressure chamber has poor effect when economic factors are considered.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the invention provides a method for optimizing the temperature control in a high-voltage room of a transformer substation, which comprises the following steps:
acquiring temperature data of the indoor equipment of the transformer substation in different operation stages, wherein the temperature acquisition period of each stage is adapted to the temperature change rate of the equipment in the corresponding stage;
taking the acquired temperature data as initial boundary temperature data, and establishing an indoor heat dissipation model, wherein the indoor heat dissipation model comprises the relation between the output of a heat dissipation device and the target temperature of indoor equipment;
determining the layout of the indoor heat dissipation device by adopting a genetic algorithm, initializing a population, wherein each individual in the population is a gene string, each gene string represents one layout of the heat dissipation device, and constructing an adaptability function by the output of the heat dissipation device and the target temperature of the indoor equipment during operation;
and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached.
Further, the temperature change rate of the device in different operation stages is represented by the load change rate of the corresponding stage, and the different operation stages of the device at least comprise:
the set time period after the equipment starts to be put into the equipment is the initial input period;
when the load satisfies the following equation, the change section is substantially balanced for the load,
；
where k is the slope of the load curve, xl is the load data of the load base balancing change section,load average value of the equalizing interval;
when the load satisfies the following formula, for the load slow-change section,
；
where x2 is the load data of the slowly varying load segment,load average value of slow variation interval;
when the load satisfies the following equation, for the load rapid change section,
；
where x3 is the load data of the load fast-change segment,is the load average value of the rapid change interval.
Further, in different operation phases of the device, the temperature acquisition cycle is specifically as follows:
in the input initial section, the temperature acquisition period is a first sampling interval, and the value of the first sampling interval is at least smaller than the sampling interval value of the load slow change section;
the load is basically balanced in the change section, and the sampling interval of the temperature acquisition period is as followsm；
In the slow load change section, the sampling interval of the temperature acquisition period is；
In the load rapid change section, the sampling interval of the temperature acquisition period is as followsWherein m and n are two different set points.
Further, the heat dissipation model is specifically as follows:
；
in the method, in the process of the invention,represents air density, ++>Represents the constant pressure heat capacity of air, k represents the heat conductivity of air, < ->For the target temperature +.>For air speed, ++>For heat generation and->The heat dissipation capacity is calculated from the total output of the heat dissipation device.
Further, the fitness function is specifically as follows:
；
in the method, in the process of the invention,w1 andw2 is the weight coefficient of the weight coefficient,for the target temperature to be optimally controlled, +.>For setting the temperature value, < >>The heat dissipation capacity is calculated from the total output of the heat dissipation device.
In a second aspect, the present invention provides a system for optimizing temperature control in a high-voltage room of a transformer substation, comprising:
the temperature acquisition unit is used for acquiring temperature data of the indoor equipment of the transformer substation in different operation stages, and the temperature acquisition period of each stage is adapted to the temperature change rate of the equipment in the corresponding stage;
the temperature calculation unit is used for taking the acquired temperature data as initial boundary temperature data, and establishing an indoor heat dissipation model which comprises the relation between the output of the heat dissipation device and the target temperature of indoor equipment;
the temperature control optimizing unit is used for determining the layout of the indoor heat dissipating device by adopting a genetic algorithm, initializing a population, wherein each individual in the population is a gene string, each gene string represents one layout of the heat dissipating device, and constructing an adaptability function by the output of the heat dissipating device and the target temperature of the indoor equipment during operation; and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached.
Further, in the temperature acquisition unit, the temperature change rate of the device in different operation stages is represented by the load change rate of the corresponding stage, and the different operation stages of the device at least include:
the set time period after the equipment starts to be put into the equipment is the initial input period;
when the load satisfies the following equation, the change section is substantially balanced for the load,
；
where k is the slope of the load curve, xl is the load data of the load base balancing change section,load average value of the equalizing interval;
when the load satisfies the following formula, for the load slow-change section,
；
where x2 is the load data of the slowly varying load segment,load average value of slow variation interval;
when the load satisfies the following equation, for the load rapid change section,
；
where x3 is the load data of the load fast-change segment,is the load average value of the rapid change interval.
Further, in the temperature acquisition unit, at different operation stages of the device, the temperature acquisition cycle is specifically as follows:
in the input initial section, the temperature acquisition period is a first sampling interval, and the value of the first sampling interval is at least smaller than the sampling interval value of the load slow change section;
the load is basically balanced in the change section, and the sampling interval of the temperature acquisition period is as followsm；
In the slow load change section, the sampling interval of the temperature acquisition period is；
In the load rapid change section, the sampling interval of the temperature acquisition period is as followsWherein m and n are two different set points.
Further, in the temperature calculation unit, the heat dissipation model is specifically as follows:
；
in the method, in the process of the invention,represents air density, ++>Represents the constant pressure heat capacity of air, k represents the heat conductivity of air, < ->For the target temperature +.>For air speed, ++>For heat generation and->The heat dissipation capacity is calculated from the total output of the heat dissipation device.
Further, in the temperature control optimizing unit, the fitness function is specifically as follows:
；
in the method, in the process of the invention,w1 andw2 is the weight coefficient of the weight coefficient,for the target temperature to be optimally controlled, +.>For setting the temperature value, < >>The heat dissipation capacity is calculated from the total output of the heat dissipation device.
In summary, the invention provides a method and a system for optimizing the temperature control in a high-voltage room of a transformer substation, which comprise the steps of collecting temperature data of indoor equipment of the transformer substation in different operation stages, wherein the temperature collection period of each stage is adapted to the temperature change rate of the equipment in the corresponding stage; taking the acquired temperature data as initial boundary temperature data, and establishing an indoor heat dissipation model, wherein the indoor heat dissipation model comprises the relation between the output of a heat dissipation device and the target temperature of indoor equipment; determining the layout of the indoor heat dissipation device by adopting a genetic algorithm, initializing a population, wherein each individual in the population is a gene string, each gene string represents one layout of the heat dissipation device, and constructing an adaptability function by the output of the heat dissipation device and the target temperature of the indoor equipment during operation; and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached. According to the invention, the temperature data of the high-pressure indoor equipment in different stages is obtained and is used as an initial boundary temperature, a heat dissipation model is constructed, and the layout of the heat dissipation device is determined by utilizing a genetic algorithm, so that a high-pressure room temperature control scheme with higher efficiency can be obtained.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for optimizing the temperature control in a high-voltage room of a transformer substation according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the embodiments described below are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a method for optimizing the temperature control in a high-voltage room of a transformer substation, which comprises the following steps:
s1: temperature data of different operation stages of the substation indoor equipment are collected, and the temperature collection period of each stage is adapted to the temperature change rate of the equipment in the corresponding stage.
It can be appreciated that when the temperature data of the device is collected, the sampling period largely determines whether the collected temperature data can truly reflect the temperature condition of the device. Because the temperature change conditions of the equipment are different in different stages, such as just when the equipment is put into operation or stably when the equipment is operated, the period of collecting the temperature is adapted to the temperature change rate of the corresponding stage, so that the collected temperature data can accurately and comprehensively reflect the heating condition of the equipment.
S2: and taking the acquired temperature data as initial boundary temperature data, and establishing an indoor heat dissipation model, wherein the indoor heat dissipation model comprises the relation between the output of a heat dissipation device and the target temperature of indoor equipment.
Taking the collected equipment temperature data as a boundary condition of a heat source, and taking the relation between the output of the heat dissipating device and the target temperature of indoor equipment into consideration, a heat dissipating model can be established and obtained. The heat dissipation model gives the relation between the power output of the indoor heat dissipation device and the target temperature of the indoor regulated equipment under the current temperature condition.
S3: the method comprises the steps of adopting a genetic algorithm to determine the layout of the indoor heat dissipation device, initializing a population, wherein each individual in the population is a gene string, each gene string represents one layout of the heat dissipation device, and constructing an adaptability function according to the output of the heat dissipation device and the target temperature of indoor equipment during operation.
The genetic algorithm (Genetic Algorithm, GA for short) originates from computer simulation research on a biological system, is a random global search optimization method, simulates the phenomena of replication, crossover (crossover) and mutation (mutation) and the like in natural selection and inheritance, starts from any initial Population (Population), generates a group of individuals more suitable for the environment through random selection, crossover and mutation operation, causes the group to evolve to better and better areas in a search space, and causes the generation to multiply and evolve continuously and finally converges to a group of individuals (indivisual) most suitable for the environment, thereby obtaining a high-quality solution of the problem.
In this embodiment, an optimization method based on a genetic algorithm is adopted to optimize the layout of the heat dissipating device, so as to achieve the goal of indoor temperature control. In this method, the gene coding scheme is defined: the layout of the heat sink is considered as a string of genes, each gene representing the location of one heat sink. In this case, a binary coding mode is adopted, and each gene is a binary number, which indicates whether the heat dissipation device is placed at the position. The initialization population is to generate a certain number of individuals (i.e., a heat dissipation layout scheme), each individual consisting of a string of binary genes representing the location of the heat dissipation device. The objective function is defined as the heat sink power output and the target temperature. Wherein, the minimized power output is to reduce energy consumption, and the target temperature is to maintain the operating temperature of the device. It may be normalized for ease of calculation.
The method for optimizing the layout of the heat dissipating device by adopting the genetic algorithm comprises the following steps:
s31: the fitness is calculated, the objective function is converted into a fitness function, and the fitness function can be defined as the sum of the two objective functions. For each individual, its corresponding temperature profile and heat dissipation power are calculated and input as a function of fitness. The fitness function needs to consider simultaneously whether the target temperature is within the set value range and whether the heat dissipation power output is minimal.
S32: and selecting, namely selecting individuals in the population according to the value of the fitness function, selecting individuals with higher fitness, and copying the individuals to the next generation.
S33: and (3) performing crossover operation on individuals in the population, and mutually exchanging part of genes for the gene strings of the two individuals. The probability of crossover can be set to an appropriate value to ensure gene diversity.
S34: mutation operation, in which individuals in a population are subjected to mutation operation, i.e., one or more genes in the individuals are randomly changed. The probability of variation can also be set to an appropriate value to ensure population diversity.
S35: generating a new population: a new population is generated by the selection, crossover and mutation operations, and then the foregoing steps are repeated until a stop condition is met.
S4: and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached.
It should be noted that the stop condition may be that the maximum number of iterations is reached or that the target fitness is reached.
The embodiment provides a substation high-voltage indoor temperature control optimization method, which comprises the steps of collecting temperature data of substation indoor equipment in different operation stages, wherein the temperature collection period of each stage is adapted to the temperature change rate of the equipment in the corresponding stage; taking the acquired temperature data as initial boundary temperature data, and establishing an indoor heat dissipation model, wherein the indoor heat dissipation model comprises the relation between the output of a heat dissipation device and the target temperature of indoor equipment; determining the layout of the indoor heat dissipation device by adopting a genetic algorithm, initializing a population, wherein each individual in the population is a gene string, each gene string represents one layout of the heat dissipation device, and constructing an adaptability function by the output of the heat dissipation device and the target temperature of the indoor equipment during operation; and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached. According to the invention, the temperature data of the high-pressure indoor equipment in different stages is obtained and is used as an initial boundary temperature, a heat dissipation model is constructed, and the layout of the heat dissipation device is determined by utilizing a genetic algorithm, so that a high-pressure room temperature control scheme with higher efficiency can be obtained.
In order to achieve the aim of accurately and comprehensively acquiring the heating condition of the equipment, in one embodiment of the invention, a segmentation method is adopted to acquire the temperature data of the equipment. The method comprises the following steps:
the temperature change rate of the equipment in different operation stages is represented by the load change rate of the corresponding stage, and the different operation stages of the equipment at least comprise:
the set time period after the equipment starts to be put into the equipment is the initial input period;
when the load satisfies the following equation, the change section is substantially balanced for the load,
；
where k is the slope of the load curve, xl is the load data of the load base balancing change section,load average value of the equalizing interval;
when the load satisfies the following formula, for the load slow-change section,
；
where x2 is the load data of the slowly varying load segment,load average value of slow variation interval;
when the load satisfies the following equation, for the load rapid change section,
；
where x3 is the load data of the load fast-change segment,is the load average value of the rapid change interval.
Based on the above-mentioned division of the operation phases of the device in the embodiment, in a further embodiment of the present invention, the temperature acquisition cycle is specifically as follows:
in the input initial section, the temperature acquisition period is a first sampling interval, and the value of the first sampling interval is at least smaller than the sampling interval value of the load slow change section;
the load is basically balanced in the change section, and the sampling interval of the temperature acquisition period is as followsm；
In the slow load change section, the sampling interval of the temperature acquisition period is；
In the rapid load change section, the temperature is acquired periodicallySampling interval isWherein m and n are two different set points.
For example, setting a start sampling section (0-1 h), and gradually increasing the temperature of the cabinet surface when the sampling section is put into operation, wherein the sampling period can be a basic period such as t=15min, 15min is also a background data acquisition interval period, and the infrared temperature measuring device updates the temperature data of the equipment. Because of the large data differences, the data updates take the form of global updates.
When the device enters the basic equilibrium change section, a long interval can be adopted, and the interval can be selected to be 60min (namely, 60min is taken by m), because of balanced load, small data change and partial update by comparing corresponding data.
When the equipment enters the slow change section, the temperature measurement period meets the following conditionsWherein 15min is the background sampling data interval, when +.>When 1/2, then 60 minutes is calculated, similarly the theory +.>If the value is 1, the minimum sampling value is calculated to be 15min.
This may take the shortest interval, chosen to be 15min, when the device enters the fast-change section.
It will be appreciated that the above sampling thermometry period of 15min or 60min is used by way of example only, and the determination of the specific time interval may be determined according to the actual situation.
Assembling all acquired temperature distribution data, e.g. single-equipment counter temperature measurement resultsN1 is the device number, the whole data is assembled as initial boundary temperature data +.>。
In another embodiment of the present invention, the layout of the heat sink (air conditioner and fan) is optimized taking the collected device temperature as a heat source boundary condition, taking into account the effect of radiant heat transfer. Establishing an indoor heat dissipation model B (X):
；
in the method, in the process of the invention,represents air density, ++>Represents the constant pressure heat capacity of air, k represents the heat conductivity of air, < ->For calculating the temperature value, < >>For air speed, ++>For heat generation and->The unit is SI, which is the heat dissipation capacity calculated by the total output of the heat dissipation device.
The relation between the heat dissipation capacity and the output of the heat dissipation device, +.>Including distribution and number of all heat sinks,/->The dynamic adjustable force of the heat sink is in the range of +.>。
In yet another embodiment of the present invention, based on the target temperature and heat sink output considerations, the following calculation may be employed as a fitness function of the genetic algorithm:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,w1 andw2 is an appropriate weight coefficient, for balancing the two targets,for the target temperature of the surface of the equipment to be optimally controlled, < + >>For setting the temperature value, < >>The heat dissipation capacity is calculated from the total output of the heat dissipation device.
The foregoing is a detailed description of an embodiment of a method for optimizing a high-voltage indoor temperature control of a transformer substation according to the present invention, and a detailed description of another embodiment of a system for optimizing a high-voltage indoor temperature control of a transformer substation according to the present invention will be provided below.
The embodiment provides a temperature control optimizing system in transformer substation high pressure room, including:
the temperature acquisition unit is used for acquiring temperature data of the indoor equipment of the transformer substation in different operation stages, and the temperature acquisition period of each stage is adapted to the temperature change rate of the equipment in the corresponding stage;
the temperature calculation unit is used for taking the acquired temperature data as initial boundary temperature data, and establishing an indoor heat dissipation model which comprises the relation between the output of the heat dissipation device and the target temperature of indoor equipment;
the temperature control optimizing unit is used for determining the layout of the indoor heat dissipating device by adopting a genetic algorithm, initializing a population, wherein each individual in the population is a gene string, each gene string represents one layout of the heat dissipating device, and constructing an adaptability function by the output of the heat dissipating device and the target temperature of the indoor equipment during operation; and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached.
Further, in the temperature acquisition unit, the temperature change rate of the device in different operation stages is represented by the load change rate of the corresponding stage, and the different operation stages of the device at least include:
the set time period after the equipment starts to be put into the equipment is the initial input period;
when the load satisfies the following equation, the change section is substantially balanced for the load,
；
where k is the slope of the load curve, xl is the load data of the load base balancing change section,load average value of the equalizing interval;
when the load satisfies the following formula, for the load slow-change section,
；
where x2 is the load data of the slowly varying load segment,load average value of slow variation interval;
when the load satisfies the following equation, for the load rapid change section,
；
where x3 is the load data of the load fast-change segment,is the load average value of the rapid change interval.
Further, in the temperature acquisition unit, at different operation stages of the device, the temperature acquisition cycle is specifically as follows:
in the input initial section, the temperature acquisition period is a first sampling interval, and the value of the first sampling interval is at least smaller than the sampling interval value of the load slow change section;
the load is basically balanced in the change section, and the sampling interval of the temperature acquisition period is as followsm；
In the slow load change section, the sampling interval of the temperature acquisition period is；
In the load rapid change section, the sampling interval of the temperature acquisition period is as followsWherein m and n are two different set points.
Further, in the temperature calculation unit, the heat dissipation model is specifically as follows:
；
in the method, in the process of the invention,represents air density, ++>Represents the constant pressure heat capacity of air, k represents the heat conductivity of air, < ->For the target temperature +.>For air speed, ++>For heat generation and->Is the heat dissipation capacity.
Further, in the temperature control optimizing unit, the fitness function is specifically as follows:
；
in the method, in the process of the invention,w1 andw2 is the weight coefficient of the weight coefficient,for the target counter temperature needing to be optimally controlled>Is the heat dissipation capacity.
It should be noted that, the optimization system provided in this embodiment is used to implement the optimization method provided in the foregoing embodiment, and specific setting of each unit is based on complete implementation of the method, which is not described herein again.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The method for optimizing the temperature control in the high-voltage room of the transformer substation is characterized by comprising the following steps of:
acquiring temperature data of the indoor equipment of the transformer substation in different operation stages, wherein the temperature acquisition period of each stage is adapted to the temperature change rate of the equipment in the corresponding stage;
taking the acquired temperature data as initial boundary temperature data, and establishing an indoor heat dissipation model, wherein the indoor heat dissipation model comprises the relation between the output of a heat dissipation device and the target temperature of indoor equipment;
determining the layout of an indoor heat dissipating device by adopting a genetic algorithm, initializing a population, wherein each individual in the population is a gene string, each gene string represents one layout of the heat dissipating device, and constructing an adaptability function according to the output of the heat dissipating device and the target temperature when the indoor equipment operates;
and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached.
2. The method for optimizing the temperature control in a high-voltage room of a transformer substation according to claim 1, wherein the temperature change rate of the equipment in different operation phases is represented by the load change rate of the corresponding phase, and the different operation phases of the equipment at least include:
the set time period after the equipment starts to be put into the equipment is the initial input period;
when the load satisfies the following equation, the change section is substantially balanced for the load,
；
where k is the slope of the load curve, xl is the load data of the load base balancing change section,load average value of the equalizing interval;
when the load satisfies the following formula, for the load slow-change section,
；
where x2 is the load data of the slowly varying load segment,load average value of slow variation interval;
when the load satisfies the following equation, for the load rapid change section,
；
where x3 is the load data of the load fast-change segment,is the load average value of the rapid change interval.
3. The method for optimizing the temperature control in a high-voltage room of a transformer substation according to claim 2, wherein the temperature acquisition period is specifically as follows in different operation stages of the equipment:
in the input initial section, the temperature acquisition period is a first sampling interval, and the value of the first sampling interval is at least smaller than the sampling interval value of the load slow change section;
the load is basically balanced in the change section, and the sampling interval of the temperature acquisition period is as followsm；
In the load slow change section, the sampling interval of the temperature acquisition period is as follows；
In the load rapid change section, the sampling interval of the temperature acquisition period is as followsWherein m and n are two different set points.
4. The method for optimizing the temperature control in a high-voltage room of a transformer substation according to claim 1, wherein the heat dissipation model is specifically as follows:
；
in the method, in the process of the invention,represents air density, ++>Represents the constant pressure heat capacity of air, k represents the heat conductivity of air, < ->For the target temperature +.>For air speed, ++>For heat generation and->The heat dissipation capacity is calculated from the total output of the heat dissipation device.
5. The method for optimizing the temperature control in a high-voltage room of a transformer substation according to claim 1, wherein the fitness function is specifically as follows:
；
in the method, in the process of the invention,w1 andw2 is the weight coefficient of the weight coefficient,for the target temperature to be optimally controlled, +.>For setting the temperature value, < >>The heat dissipation capacity is calculated from the total output of the heat dissipation device.
6. A substation high-voltage indoor temperature control optimization system, comprising:
the temperature acquisition unit is used for acquiring temperature data of the indoor equipment of the transformer substation in different operation stages, and the temperature acquisition period of each stage is adapted to the temperature change rate of the equipment in the corresponding stage;
the temperature calculation unit is used for taking the acquired temperature data as initial boundary temperature data and establishing an indoor heat dissipation model, wherein the indoor heat dissipation model comprises the relation between the output of a heat dissipation device and the target temperature of indoor equipment;
the temperature control optimizing unit is used for determining the layout of the indoor heat dissipating device by adopting a genetic algorithm, initializing a population, wherein each individual in the population is a gene string, each gene string represents one layout of the heat dissipating device, and constructing an adaptability function according to the output of the heat dissipating device and the target temperature when the indoor equipment operates; and continuously iterating to update the population, and selecting an individual with the optimal fitness as a current indoor temperature control scheme until a stopping condition is reached.
7. The system according to claim 6, wherein in the temperature acquisition unit, the temperature change rate of the device in different operation phases is represented by a load change rate of a corresponding phase, and the different operation phases of the device at least include:
the set time period after the equipment starts to be put into the equipment is the initial input period;
when the load satisfies the following equation, the change section is substantially balanced for the load,
；
where k is the slope of the load curve, xl is the load data of the load base balancing change section,load average value of the equalizing interval;
when the load satisfies the following formula, for the load slow-change section,
；
where x2 is the load data of the slowly varying load segment,load average value of slow variation interval;
when the load satisfies the following equation, for the load rapid change section,
；
where x3 is the load data of the load fast-change segment,is the load average value of the rapid change interval.
8. The system according to claim 7, wherein in the temperature acquisition unit, at different operation phases of the device, the temperature acquisition cycle is specifically as follows:
in the input initial section, the temperature acquisition period is a first sampling interval, and the value of the first sampling interval is at least smaller than the sampling interval value of the load slow change section;
the load is basically balanced in the change section, and the sampling interval of the temperature acquisition period is as followsm；
In the load slow change section, the sampling interval of the temperature acquisition period is as follows；
In the load rapid change section, the sampling interval of the temperature acquisition period is as followsWherein m and n are two different set points.
9. The system according to claim 6, wherein in the temperature calculation unit, the heat dissipation model is specifically as follows:
；
in the method, in the process of the invention,represents air density, ++>Represents the constant pressure heat capacity of air, k represents the heat conductivity of air, < ->For the target temperature +.>For air speed, ++>For heat generation and->The heat dissipation capacity is calculated from the total output of the heat dissipation device.
10. The system according to claim 6, wherein in the temperature control optimizing unit, the fitness function is specifically as follows:
；
in the method, in the process of the invention,w1 andw2 is the weight coefficient of the weight coefficient,for the target temperature to be optimally controlled, +.>For setting the temperature value, < >>The heat dissipation capacity is calculated from the total output of the heat dissipation device.
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