CN113570116A - Method for estimating heating power of storage and charging station and terminal - Google Patents
Method for estimating heating power of storage and charging station and terminal Download PDFInfo
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Abstract
The invention discloses a method and a terminal for estimating heating power of a storage and charging station, which can acquire the heating power under each working condition by counting working condition data and temperature change values of the storage and charging station within preset time and calculating the heating power based on the temperature change values, and record the heating power into corresponding record set according to the working condition; weighting the heating power in the record set according to the storage time of the heating power, judging whether the weighted record set is a credible record set, calculating a probability distribution function of the credible record set, and realizing the estimation of the heating power according to the probability distribution function; therefore, the distribution function of the heating power under the working condition can be predicted by a method combining the record set weighting and the probability distribution function prediction, so that the heating power is estimated, and the estimation accuracy is improved.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to a method and a terminal for estimating heating power of a storage and charging station.
Background
The storage and charging station needs to carry out one or more times of energy conversion inside, so that a heating phenomenon exists, when all parts of the storage and charging station are located in a closed space, the closed space comprises but is not limited to a container, the heating power inside the storage and charging station needs to be accurately estimated, and the heating power is led out to the outside of the system through a reasonable mode, so that the continuous temperature rise of the system is avoided, and the normal operation of the station is influenced.
Due to the problems that the operation condition change combination of various components in the storage and charging station is complex, the component efficiency at different time intervals of different stations is different, the heating power is calculated by measuring the temperature, the hysteresis is caused, and the like, the heating power of the system is difficult to accurately estimate.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the terminal for estimating the heating power of the storage and charging station can improve the estimation accuracy.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for estimating the heating power of a storage and charging station comprises the following steps:
counting working condition data and temperature change values of the storage and charging station within preset time, calculating heating power of the storage and charging station according to the temperature change values, and storing the heating power in a record set corresponding to the working condition data;
weighting the heating power in the record set according to the storage time of the heating power, judging whether the record set after weighting is a credible record set, if so, calculating a probability distribution function of the credible record set, and estimating the heating power according to the probability distribution function.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
an estimation terminal for a charging station heating power, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
counting working condition data and temperature change values of the storage and charging station within preset time, calculating heating power of the storage and charging station according to the temperature change values, and storing the heating power in a record set corresponding to the working condition data;
weighting the heating power in the record set according to the storage time of the heating power, judging whether the record set after weighting is a credible record set, if so, calculating a probability distribution function of the credible record set, and estimating the heating power according to the probability distribution function.
The invention has the beneficial effects that: calculating heating power based on the temperature change value by counting the working condition data and the temperature change value of the storage and charging station within the preset time, acquiring the heating power under each working condition, and recording the heating power into a corresponding record set according to the working condition; weighting the heating power in the record set according to the storage time of the heating power, judging whether the weighted record set is a credible record set, calculating a probability distribution function of the credible record set, and realizing the estimation of the heating power according to the probability distribution function; therefore, the distribution function of the heating power under the working condition can be predicted by a method combining the record set weighting and the probability distribution function prediction, so that the heating power is estimated, and the estimation accuracy is improved.
Drawings
Fig. 1 is a flowchart of a method for estimating a thermal power of a storage station according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a terminal for estimating the heating power of a charging station according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating steps of a method for estimating a thermal power of a storage station according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an internal structure of a storage and charging station of a method for estimating a heating power of the storage and charging station according to an embodiment of the present invention.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1 and fig. 3, an embodiment of the present invention provides a method for estimating a heating power of a storage station, including:
counting working condition data and temperature change values of the storage and charging station within preset time, calculating heating power of the storage and charging station according to the temperature change values, and storing the heating power in a record set corresponding to the working condition data;
weighting the heating power in the record set according to the storage time of the heating power, judging whether the record set after weighting is a credible record set, if so, calculating a probability distribution function of the credible record set, and estimating the heating power according to the probability distribution function.
From the above description, the beneficial effects of the present invention are: calculating heating power based on the temperature change value by counting the working condition data and the temperature change value of the storage and charging station within the preset time, acquiring the heating power under each working condition, and recording the heating power into a corresponding record set according to the working condition; weighting the heating power in the record set according to the storage time of the heating power, judging whether the weighted record set is a credible record set, calculating a probability distribution function of the credible record set, and realizing the estimation of the heating power according to the probability distribution function; therefore, the distribution function of the heating power under the working condition can be predicted by a method combining the record set weighting and the probability distribution function prediction, so that the heating power is estimated, and the estimation accuracy is improved.
Further, the weighting the heating power in the recording set according to the storage time of the heating power includes:
acquiring the time difference between the storage time of each heating power record in the record set and the current time, and sequencing each heating power record from small to large according to the time difference;
weighting the heating power in the record set in sequence, and gradually reducing the weighted weight;
determining whether the weighted record set is a trusted record set comprises:
calculating the average value and standard deviation of all the heating powers in the weighted record set;
and judging whether the absolute value of the difference value between the heating power exceeding the preset proportion and the average value in the record set is smaller than two standard deviations, if so, the record set is a credible record set, and otherwise, the record set is an incredible record set.
As can be seen from the above description, the heating power is weighted according to the time difference between the storage time of the heating power record and the current time, and the smaller the time difference is, the larger the weighted weight is, so that the calculation can be performed based on the latest heating record; and meanwhile, determining whether the record set is a credible record set or not by using the weighted average value and standard deviation of the record set, and when the absolute value of the difference value between the heating power exceeding a preset proportion and the average value is smaller than two standard deviations, considering that the heating power fluctuation under the same working condition data is smaller, so that the record set has a reference meaning, otherwise, indicating that the fluctuation of the heating power under the same working condition data is larger, and the record set does not have the reference meaning, thereby obtaining the credible record set of the data and being capable of maximally improving the estimation accuracy.
Further, the estimating of the heating power according to the probability distribution function includes:
obtaining probability distribution conditions of different heating powers under corresponding working condition data according to the probability distribution function;
and combining the probability distribution condition and the operation condition of the storage and charging station to select the heating power of the storage and charging station.
From the above description, the heating power of the storage and charging station is selected by combining the probability distribution condition and the operation condition of the storage and charging station, and whether a normal value with a higher probability or an extreme value with a lower probability in the probability distribution needs to be selected can be judged based on the operation condition of the storage and charging station, so that the method is suitable for various heating power estimation scenes.
Further, calculating the heating power of the storage and charging station according to the temperature variation value comprises:
inquiring a relation table of temperature change and heating value according to the temperature change value to obtain the heating value corresponding to the temperature change;
and calculating the heating power of the storage and charging station according to the heating value.
According to the above description, the heating power of the storage and charging station is determined by determining the heating value corresponding to the temperature change value through the relationship table of the temperature change and the heating value, the heating power under various working conditions can be calculated in advance, and the subsequent estimation based on the heating power obtained through calculation is facilitated.
Further, the counting the temperature change value of the storage and charging station within the preset time includes:
finding out key points of the storage and charging station according to the distribution of the thermal field, and placing temperature sensors on the key points;
calculating the average value and standard deviation of all the heating powers in the weighted record set;
according to the description, the key points of the storage and charging station are found out through the field distribution, and the temperature sensors are placed on the key points to measure representative temperature values in the storage and charging station, so that the temperature change of the storage and charging station within the preset time is accurately obtained, and the temperature measurement precision is improved.
Referring to fig. 2, another embodiment of the present invention provides an estimation terminal for a heat power of a charging station, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the following steps:
counting working condition data and temperature change values of the storage and charging station within preset time, calculating heating power of the storage and charging station according to the temperature change values, and storing the heating power in a record set corresponding to the working condition data;
weighting the heating power in the record set according to the storage time of the heating power, judging whether the record set after weighting is a credible record set, if so, calculating a probability distribution function of the credible record set, and estimating the heating power according to the probability distribution function.
According to the description, the heating power under each working condition can be collected by counting the working condition data and the temperature change value of the storage and charging station within the preset time and calculating the heating power based on the temperature change value, and the heating power is recorded into the corresponding record set according to the working condition; weighting the heating power in the record set according to the storage time of the heating power, judging whether the weighted record set is a credible record set, calculating a probability distribution function of the credible record set, and realizing the estimation of the heating power according to the probability distribution function; therefore, the distribution function of the heating power under the working condition can be predicted by a method combining the record set weighting and the probability distribution function prediction, so that the heating power is estimated, and the estimation accuracy is improved.
Further, the weighting the heating power in the recording set according to the storage time of the heating power includes:
acquiring the time difference between the storage time of each heating power record in the record set and the current time, and sequencing each heating power record from small to large according to the time difference;
weighting the heating power in the record set in sequence, and gradually reducing the weighted weight;
determining whether the weighted record set is a trusted record set comprises:
calculating the average value and standard deviation of all the heating powers in the weighted record set;
and judging whether the absolute value of the difference value between the heating power exceeding the preset proportion and the average value in the record set is smaller than two standard deviations, if so, the record set is a credible record set, and otherwise, the record set is an incredible record set.
As can be seen from the above description, the heating power is weighted according to the time difference between the storage time of the heating power record and the current time, and the smaller the time difference is, the larger the weighted weight is, so that the calculation can be performed based on the latest heating record; and meanwhile, determining whether the record set is a credible record set or not by using the weighted average value and standard deviation of the record set, and when the absolute value of the difference value between the heating power exceeding a preset proportion and the average value is smaller than two standard deviations, considering that the heating power fluctuation under the same working condition data is smaller, so that the record set has a reference meaning, otherwise, indicating that the fluctuation of the heating power under the same working condition data is larger, and the record set does not have the reference meaning, thereby obtaining the credible record set of the data and being capable of maximally improving the estimation accuracy.
Further, the estimating of the heating power according to the probability distribution function includes:
obtaining probability distribution conditions of different heating powers under corresponding working condition data according to the probability distribution function;
and combining the probability distribution condition and the operation condition of the storage and charging station to select the heating power of the storage and charging station.
From the above description, the heating power of the storage and charging station is selected by combining the probability distribution condition and the operation condition of the storage and charging station, and whether a normal value with a higher probability or an extreme value with a lower probability in the probability distribution needs to be selected can be judged based on the operation condition of the storage and charging station, so that the method is suitable for various heating power estimation scenes.
Further, calculating the heating power of the storage and charging station according to the temperature variation value comprises:
inquiring a relation table of temperature change and heating value according to the temperature change value to obtain the heating value corresponding to the temperature change;
and calculating the heating power of the storage and charging station according to the heating value.
According to the above description, the heating power of the storage and charging station is determined by determining the heating value corresponding to the temperature change value through the relationship table of the temperature change and the heating value, the heating power under various working conditions can be calculated in advance, and the subsequent estimation based on the heating power obtained through calculation is facilitated.
Further, the counting the temperature change value of the storage and charging station within the preset time includes:
finding out key points of the storage and charging station according to the distribution of the thermal field, and placing temperature sensors on the key points;
and calculating the temperature change of the temperature sensor within the preset time.
According to the description, the key points of the storage and charging station are found out through the field distribution, and the temperature sensors are placed on the key points to measure representative temperature values in the storage and charging station, so that the temperature change of the storage and charging station within the preset time is accurately obtained, and the temperature measurement precision is improved.
The method and the terminal for estimating the heating power of the storage and charging station are suitable for estimating the heating power of various closed storage and charging stations and improving the accuracy of heating power prediction, so that reasonable heat dissipation power can be selected, the temperature inside the station is well controlled, and the heat dissipation energy consumption is low, and are explained by a specific implementation mode as follows:
example one
Referring to fig. 1, fig. 3 and fig. 4, a method for estimating a heating power of a storage station includes the steps of:
s1, counting working condition data and temperature change values of the storage and charging station within preset time, calculating heating power of the storage and charging station according to the temperature change values, and storing the heating power in a record set corresponding to the working condition data.
S11, counting the temperature change values of the storage and charging station in the preset time, wherein the temperature change values comprise:
finding out key points of the storage and charging station according to the distribution of the thermal field, and placing temperature sensors on the key points;
and calculating the temperature change of the temperature sensor within the preset time.
Specifically, referring to fig. 4, the key points of the storage and charging station are found out in the distribution of the thermal field, and the temperature sensors are placed on the key points, so that the temperature change coordinates of the key points can be used to represent the temperature change of the whole storage and charging station, so as to calculate the heating power.
S12, calculating the heating power of the storage and charging station according to the temperature change value comprises the following steps:
inquiring a relation table of temperature change and heating value according to the temperature change value to obtain the heating value corresponding to the temperature change;
and calculating the heating power of the storage and charging station according to the heating value.
Specifically, setting the statistical time period of the heating power to be m minutes, judging whether a complete statistical time period n elapses in the running process of the equipment, and if the complete time period elapses, counting the output power P1 of a PCS (energy storage converter), the output power P2 of a DCDC (direct current converter), the output power P3 of a battery pack and the cooling power P4 of a refrigeration system in the time period;
according to the temperature change in the period, the heat generation amount Wm corresponding to the temperature change in the period is calculated by combining the temperature change and the heat generation amount relation table, the heat generation power Pm is equal to Wm/m, and P1, P2, P3, P4 and Pm are collectively referred to as a record Pn.
Searching a heating power record set S consistent with P1, P2, P3 and P4 which are working condition data from the history record, and if the S set is found, adding the Pn record into the S set; and if the S set is not found, adding the Pn record into the new S set after the S set is newly created.
S2, weighting the heating power in the record set according to the storage time of the heating power, judging whether the record set after weighting is a credible record set, if so, calculating the probability distribution function of the credible record set, and estimating the heating power according to the probability distribution function.
S21, estimating the heating power according to the probability distribution function includes:
obtaining probability distribution conditions of different heating powers under corresponding working condition data according to the probability distribution function;
and combining the probability distribution condition and the operation condition of the storage and charging station to select the heating power of the storage and charging station.
Specifically, for the credible set, the probability distribution function of the S set needs to be calculated and stored; by combining the probability distribution condition of the probability distribution function and the operation condition of the storage and charging station, whether a normal value with a higher probability or an extreme value with a lower probability in the probability distribution needs to be selected or not can be judged based on the operation condition of the storage and charging station, so that the method is suitable for various scenes of heating power estimation and realizes accurate heating power estimation.
Example two
The difference between this embodiment and the first embodiment is that how to determine whether the heating power set is reliable is further defined, specifically:
the weighting the heating power in the recording set according to the storage time of the heating power includes:
acquiring the time difference between the storage time of each heating power record in the record set and the current time, and sequencing each heating power record from small to large according to the time difference;
and weighting the heating power in the record set in sequence, and gradually reducing the weighted weight.
In this embodiment, the weight values of the records in the S set are updated according to the recording time, and the weight value updating method includes: the smaller the time difference between the record and the current time is, the larger the weight is;
and calculating the time difference between the storage time of each heating power record in the record set S and the current time, sequencing each heating power record from small to large according to the time difference, sequentially weighting the heating power in the record set S, and gradually reducing the weighted weight. For example, if there is a heating power in the record set at today's zero point, the weighting weight is 10, and if there is another heating power at yesterday's zero point, the weighting weight is 9, and so on, and the specific defined numerical values are only used for illustration.
Determining whether the weighted record set is a trusted record set comprises:
calculating the average value and standard deviation of all the heating powers in the weighted record set;
and judging whether the absolute value of the difference value between the heating power exceeding the preset proportion and the average value in the record set is smaller than two standard deviations, if so, the record set is a credible record set, and otherwise, the record set is an incredible record set.
Specifically, in the present embodiment, the average value avg and the standard deviation σ of the heat generation power in the S set are calculated;
and if the absolute value of the difference value between the heating power exceeding 95% in the S set and the average value avg is less than 2 sigma, judging the S set as a credible set, otherwise, judging the S set as an incredible set. And marking that the S set belongs to a credible set or an incredible set according to the conclusion.
EXAMPLE III
Referring to fig. 2, an estimation terminal for heat power of a charging station includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the steps of the estimation method for heat power of a charging station according to the first embodiment or the second embodiment.
In summary, according to the estimation method and the terminal for the heating power of the storage and charging station provided by the invention, the working condition data and the temperature change value of the storage and charging station within the preset time are counted, the heating power is calculated based on the temperature change value, the heating power under each working condition can be collected, and the heating power is recorded into the corresponding record set according to the working condition, wherein the key points of the storage and charging station are found out based on the thermal field distribution, the temperature measurement is carried out on the key points, the temperature change of the key points can be used for representing the temperature change of the whole storage and charging station, and the calculation difficulty is reduced; weighting the heating power in the record set according to the storage time of the heating power, judging whether the weighted record set is a credible record set or not, calculating a probability distribution function of the credible record set, and realizing the estimation of the heating power according to the probability distribution function, wherein the heating power can be estimated according to the probability distribution function and the operation condition of the storage and charging station, so that a normal value with high probability or an extreme value with low probability can be obtained, the method is suitable for the estimation of the heating power of various storage and charging stations, and the estimation accuracy is improved; therefore, the distribution function of the heating power under the working condition can be predicted by a method combining the record set weighting and the probability distribution function prediction, so that the heating power is estimated, and the estimation accuracy is improved.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for estimating the heating power of a storage and charging station is characterized by comprising the following steps:
counting working condition data and temperature change values of the storage and charging station within preset time, calculating heating power of the storage and charging station according to the temperature change values, and storing the heating power in a record set corresponding to the working condition data;
weighting the heating power in the record set according to the storage time of the heating power, judging whether the record set after weighting is a credible record set, if so, calculating a probability distribution function of the credible record set, and estimating the heating power according to the probability distribution function.
2. The method of claim 1, wherein the weighting the heating power in the record set according to the storage time of the heating power comprises:
acquiring the time difference between the storage time of each heating power record in the record set and the current time, and sequencing each heating power record from small to large according to the time difference;
weighting the heating power in the record set in sequence, and gradually reducing the weighted weight;
determining whether the weighted record set is a trusted record set comprises:
calculating the average value and standard deviation of all the heating powers in the weighted record set;
and judging whether the absolute value of the difference value between the heating power exceeding the preset proportion and the average value in the record set is smaller than two standard deviations, if so, the record set is a credible record set, and otherwise, the record set is an incredible record set.
3. The method of claim 1, wherein the estimating the thermal power of the storage station according to the probability distribution function comprises:
obtaining probability distribution conditions of different heating powers under corresponding working condition data according to the probability distribution function;
and combining the probability distribution condition and the operation condition of the storage and charging station to select the heating power of the storage and charging station.
4. The method of claim 1, wherein calculating the thermal power of the storage station according to the temperature variation value comprises:
inquiring a relation table of temperature change and heating value according to the temperature change value to obtain the heating value corresponding to the temperature change;
and calculating the heating power of the storage and charging station according to the heating value.
5. The method for estimating the heating power of the storage and charging station according to any one of claims 1 to 4, wherein the counting the temperature variation value of the storage and charging station within a preset time comprises:
finding out key points of the storage and charging station according to the distribution of the thermal field, and placing temperature sensors on the key points;
and calculating the temperature change of the temperature sensor within the preset time.
6. An estimation terminal for a heating power of a charging station, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the following steps when executing the computer program:
counting working condition data and temperature change values of the storage and charging station within preset time, calculating heating power of the storage and charging station according to the temperature change values, and storing the heating power in a record set corresponding to the working condition data;
weighting the heating power in the record set according to the storage time of the heating power, judging whether the record set after weighting is a credible record set, if so, calculating a probability distribution function of the credible record set, and estimating the heating power according to the probability distribution function.
7. The terminal for estimating thermal power of a storage station according to claim 6, wherein said weighting the thermal power in the record set according to the storage time of the thermal power comprises:
acquiring the time difference between the storage time of each heating power record in the record set and the current time, and sequencing each heating power record from small to large according to the time difference;
weighting the heating power in the record set in sequence, and gradually reducing the weighted weight;
determining whether the weighted record set is a trusted record set comprises:
calculating the average value and standard deviation of all the heating powers in the weighted record set;
and judging whether the absolute value of the difference value between the heating power exceeding the preset proportion and the average value in the record set is smaller than two standard deviations, if so, the record set is a credible record set, and otherwise, the record set is an incredible record set.
8. The terminal of claim 6, wherein the estimating the heating power of the storage station according to the probability distribution function comprises:
obtaining probability distribution conditions of different heating powers under corresponding working conditions according to the probability distribution function;
and combining the probability distribution condition and the operation condition of the storage and charging station to select the heating power of the storage and charging station.
9. The terminal of claim 6, wherein the estimating the heating power of the storage station according to the probability distribution function comprises:
obtaining probability distribution conditions of different heating powers under corresponding working condition data according to the probability distribution function;
and combining the probability distribution condition and the operation condition of the storage and charging station to select the heating power of the storage and charging station.
10. The terminal for estimating the heating power of the storage station according to any one of claims 6 to 9, wherein the counting the temperature variation values of the storage station within the preset time includes:
finding out key points of the storage and charging station according to the distribution of the thermal field, and placing temperature sensors on the key points;
and calculating the temperature change of the temperature sensor within the preset time.
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| PCT/CN2021/118997 WO2023272956A1 (en) | 2021-07-02 | 2021-09-17 | Method and terminal for estimating heating power of storage and charging station |
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| CN114939926A (en) * | 2021-12-13 | 2022-08-26 | 湖南顺通能源科技有限公司 | Waste heat utilization monitoring system of ceramic body drying room |
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