A method for evaluating state of charge in digital power grid

Abstract

The present invention relates to the technical field of charge state analysis, and specifically to a charge state assessment method for a digital power grid; determining an abnormal peak point according to the data fluctuation characteristics of the peak point in a current curve; obtaining the interference degree and the current abnormal point according to the distance characteristics of the abnormal peak point and the data change characteristics between the abnormal peak point and the adjacent trough point; obtaining the current abnormal change sequence according to the data difference between adjacent current abnormal points; obtaining the temperature change characteristic sequence according to the temperature curve; obtaining the similarity degree according to the current abnormal change sequence and the temperature change characteristic sequence; obtaining the adjustment coefficient according to the similarity degree, the distance characteristics of the current abnormal point closest to the current moment, and the interference degree. The present invention adjusts the preset coulomb efficiency according to the adjustment coefficient to obtain an adaptive coulomb efficiency and calculates the charge state value at the next moment, thereby improving the calculation accuracy of the charge state.

Description

Technical Field

[0001] The present invention relates to the technical field of charge state analysis, and in particular to a charge state evaluation method for a digital power grid. Background Art

[0002] State of charge (SOC) refers to the ratio of the capacity that can be released by the battery under the prescribed discharge conditions to the capacity in the fully charged state; grid batteries are used to store the extra power generated by the grid during the off-peak period and release power during the peak period. The state of charge of the grid battery reflects the remaining capacity of the battery and is one of the important parameters for evaluating the battery status; accurately evaluating the state of charge helps to improve the safety and service life of grid batteries, as well as improve the accuracy of grid adjustment. The existing commonly used ampere-hour integration method is used to calculate the state of charge of the battery. This method obtains the state of charge based on the fixed coulomb efficiency and the measured current value. However, for batteries with long-term charge and discharge cycles in the grid, temperature anomalies are prone to occur. Temperature anomalies will lead to reduced coulomb efficiency, which makes it easy for the state of charge calculated based on the fixed coulomb efficiency to have deviations, which ultimately leads to low accuracy in evaluating the state of charge of batteries in the grid using the ampere-hour integration method. Summary of the invention

[0003] In order to solve the technical problem that the fixed coulomb efficiency in the above-mentioned ampere-hour integration method leads to low accuracy of the state of charge assessment of the battery in the power grid, the purpose of the present invention is to provide a method for assessing the state of charge of a digital power grid, and the technical solution adopted is as follows:

[0004] Obtain the current state of charge value of the battery, the current curve graph and the temperature curve graph of the recent historical period;

[0005] Obtain an abnormal characteristic value according to the data fluctuation characteristics of the peak point in the current curve; obtain an abnormal peak point according to the abnormal characteristic value of the peak point; obtain the interference degree according to the distance characteristics between the abnormal peak point and the adjacent abnormal peak point, and the data change characteristics between the abnormal peak point and the adjacent trough point; determine the current abnormal point according to the interference degree of the abnormal peak point;

[0006] Obtain a current abnormal change sequence according to the data difference characteristics between adjacent current abnormal points; obtain a temperature change feature sequence according to the difference characteristics between the temperature data at the same time as the current abnormal point in the temperature curve; obtain a similarity degree according to the change similarity characteristics between the current abnormal change sequence and the temperature change feature sequence;

[0007] An adjustment coefficient is obtained according to the similarity, the distance characteristics of the current anomaly point closest to the current moment and the interference degree; the preset coulomb efficiency is adjusted according to the adjustment coefficient to obtain an adaptive coulomb efficiency; and the charge state value at the next moment is obtained by the ampere-hour integration method according to the charge state value at the current moment and the adaptive coulomb efficiency.

[0008] Furthermore, the step of obtaining an abnormal characteristic value according to the data fluctuation characteristics of the peak point in the current curve graph includes:

[0009] The average value of the numerical difference between the peak point and adjacent trough points at the previous and next moments is calculated and normalized to obtain the abnormal characteristic value of the peak point.

[0010] Furthermore, the step of obtaining an abnormal peak point according to the abnormal characteristic value of the peak point includes:

[0011] The peak point where the abnormal characteristic value exceeds the preset abnormal threshold is taken as the abnormal peak point.

[0012] Furthermore, the step of obtaining the interference degree according to the distance characteristics between the abnormal peak point and the adjacent abnormal peak point and the data change characteristics between the abnormal peak point and the adjacent trough point includes:

[0013] Calculate the average value of the time interval between the abnormal peak point and the adjacent abnormal peak point to obtain the interval characteristic value; for any data point between the abnormal peak point and the adjacent trough point, calculate the absolute value of the difference between the tangent slope of the arbitrary data point and the adjacent data point to obtain the change characteristic value; calculate the sum of the change characteristic values ​​of all data points between the abnormal peak point and the adjacent trough point to obtain the cumulative change characteristic value; calculate the product of the reciprocal of the interval characteristic value and the reciprocal of the cumulative change characteristic value to obtain the interference degree of the abnormal peak point.

[0014] Furthermore, the step of determining the abnormal current point according to the interference degree of the abnormal wave peak point includes:

[0015] The abnormal peak point whose interference degree exceeds a preset interference threshold is taken as the current abnormal point.

[0016] Furthermore, the step of obtaining the current abnormal change sequence according to the data difference characteristics between adjacent current abnormal points includes:

[0017] The numerical difference between any current anomaly point and the current anomaly point at adjacent historical moments is calculated to obtain adjacent difference values ​​of the any current anomaly point, and the adjacent difference values ​​of all current anomaly points are sorted in chronological order to obtain the current anomaly change sequence.

[0018] Furthermore, the step of obtaining a temperature change characteristic sequence according to the difference characteristics between the temperature data at the same time as the current abnormal point in the temperature curve diagram comprises:

[0019] In the temperature curve diagram, the temperature at the time when the current abnormal point is located is used as the marked temperature; the difference between any marked temperature and the marked temperature at the adjacent historical moment is calculated to obtain the temperature difference value; the temperature difference values ​​of all marked temperatures are sorted in actual order to obtain the temperature change characteristic sequence.

[0020] Furthermore, the step of obtaining the similarity degree according to the similarity characteristics of the changes of the abnormal current change sequence and the temperature change characteristic sequence includes:

[0021] The absolute value of the Pearson correlation coefficient between the abnormal current change sequence and the temperature change characteristic sequence is calculated to obtain the similarity degree.

[0022] Furthermore, the step of obtaining the adjustment coefficient according to the similarity, the distance characteristic of the current abnormal point closest to the current moment and the interference degree includes:

[0023] Calculate the average of the time intervals of all adjacent current abnormal points to obtain a time length value; if there is no current abnormal point within the historical time range of the time length value adjacent to the current moment, the adjustment coefficient of the current moment is a constant of 1;

[0024] If there is a current anomaly point within the historical time range of the time length value adjacent to the current moment, calculate the product of the interference degree and the similarity degree of the current anomaly point closest to the current moment to obtain the temperature influence characteristic value; calculate the inverse of the time interval between the current moment and the current anomaly point closest to the current moment to obtain the distance weight; calculate the product of the temperature influence characteristic value, the distance weight, and the preset adjustment reference to obtain the adjustment degree; calculate the difference between the constant 1 and the adjustment degree to obtain the adjustment coefficient.

[0025] Furthermore, the step of adjusting the preset coulombic efficiency according to the adjustment coefficient to obtain the adaptive coulombic efficiency includes:

[0026] The product of the adjustment coefficient and the preset coulombic efficiency is calculated to obtain the adaptive coulombic efficiency.

[0027] The present invention has the following beneficial effects:

[0028] In the present invention, obtaining abnormal characteristic values ​​can be used to preliminarily screen the moment of current fluctuation, obtaining abnormal peak points can determine the moment of abnormal current fluctuation, and then judging whether the current is affected by temperature anomaly according to abnormal peak points. The current fluctuation characteristics of the battery caused by temperature anomaly are different from those caused by other situations. Therefore, the interference degree can be obtained according to the distance characteristics between the abnormal peak point and the adjacent abnormal peak point, and the data change characteristics between the abnormal peak point and the adjacent trough point, which can characterize the possible degree of abnormal peak point caused by temperature anomaly; obtaining the current abnormal point can preliminarily determine the moment caused by temperature anomaly. Obtaining the abnormal current change sequence and the temperature change characteristic sequence can further analyze the correlation between the current anomaly and the temperature anomaly, improve the calculation accuracy of the final state of charge, and obtaining the similarity degree can characterize the correlation between the current abnormal point and the temperature anomaly, thereby improving the analysis accuracy of the influence of temperature anomaly on the charging and discharging of the battery. Obtaining the adjustment coefficient and the adaptive coulomb efficiency can adjust the coulomb efficiency according to the characteristics of the battery affected by temperature anomaly, so that the coulomb efficiency is adaptively adjusted according to the degree of temperature anomaly, thereby improving the accuracy of the state of charge calculated by the ampere-hour integration method. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 A flow chart of a method for assessing the state of charge of a digital power grid provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the charge state assessment method for a digital power grid proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0032] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0033] A specific scheme of a method for assessing the state of charge of a digital power grid provided by the present invention is described in detail below with reference to the accompanying drawings.

[0034] See also Figure 1 , which shows a flow chart of a method for assessing the state of charge of a digital power grid provided by an embodiment of the present invention, the method comprising the following steps:

[0035] Step S1, obtaining the current state of charge value of the battery, and the current curve graph and temperature curve graph of the recent preset historical period.

[0036] In the embodiment of the present invention, the implementation scenario is to evaluate the state of charge of the battery in the power grid to improve the accuracy of the evaluation. First, the state of charge value of the battery at the current moment, the current curve graph and the temperature curve graph of the preset recent historical period are obtained; the horizontal axis of the current curve graph and the temperature curve graph is the time, and the vertical axis is the value corresponding to different moments; in the embodiment of the present invention, the preset recent historical period is the adjacent 30 minutes of the current moment, and the acquisition frequency is 60 Hz, which can be set by the implementer according to the implementation scenario; with the update of the current moment, the data of the preset recent historical period changes synchronously.

[0037] Under normal circumstances, the state of charge of a battery will change slowly with the change of the amount of electricity. At this time, the state of charge value can be accurately calculated by the ampere-hour integration method based on the fixed coulomb efficiency. When the external temperature is abnormal, the coulomb efficiency will change, which will make the accuracy of the state of charge calculated by the existing ampere-hour integration method low. Therefore, in order to improve the accuracy of the state of charge calculation of the ampere-hour integration method under abnormal temperature conditions, it is necessary to improve the value of the coulomb efficiency under different degrees of temperature abnormality.

[0038] Step S2, obtaining an abnormal characteristic value according to the data fluctuation characteristics of the peak point in the current curve diagram; obtaining an abnormal peak point according to the abnormal characteristic value of the peak point; obtaining the degree of interference according to the distance characteristics between the abnormal peak point and the adjacent abnormal peak point, and the data change characteristics between the abnormal peak point and the adjacent trough point; determining the current abnormal point according to the degree of interference of the abnormal peak point.

[0039] If the battery itself or the ambient temperature is abnormal, the current will change, and then the battery temperature control system will adjust the battery temperature to make it as stable as possible within the appropriate temperature range, which will cause the current to fluctuate in the process. Therefore, the period of time where temperature anomalies may exist can be preliminarily screened through the current fluctuation characteristics, so the abnormal characteristic value is obtained according to the data fluctuation characteristics of the peak point in the current curve; preferably, in the embodiment of the present invention, the step of obtaining the abnormal characteristic value includes: calculating the average value of the numerical difference between the peak point and the adjacent trough points before and after the moment and normalizing it to obtain the abnormal characteristic value of the peak point; in the embodiment of the present invention, normalization is performed by the existing maximum and minimum value normalization method. The adjacent trough points are the two trough points closest to the peak point before and after the moment. When the abnormal characteristic value is larger, it means that the current fluctuation in the period near this moment is more abnormal, and the less it conforms to the current fluctuation characteristics under normal load, and the more likely it is caused by temperature anomalies. Furthermore, the abnormal peak point can be obtained according to the abnormal characteristic value of the peak point, specifically including: taking the peak point whose abnormal characteristic value exceeds the preset abnormal threshold as the abnormal peak point. In the embodiment of the present invention, the preset abnormal threshold is 0.5, which can be determined by the implementer according to the implementation scenario; the abnormal peak point reflects the moment when the current change is relatively abnormal.

[0040] Furthermore, due to the high frequency of charge and discharge conversion of batteries in the power grid and the different power generation efficiency of distributed photovoltaic nodes, the batteries may also have large fluctuations under normal circumstances; therefore, the abnormal peak point cannot fully represent the temperature abnormality, and further analysis of the abnormal peak point is required. When the temperature is abnormal, the current changes, and then the temperature system of the battery regulates it to keep the temperature as stable as possible within the normal range. In this process, the current will have frequent abnormal fluctuations; under normal circumstances, the large fluctuations of the current are relatively random and it is difficult to have long-term fluctuations. Therefore, the degree of interference can be obtained based on the distance characteristics between the abnormal peak point and the adjacent abnormal peak point, and the data change characteristics between the abnormal peak point and the adjacent trough point.

[0041] Preferably, in an embodiment of the present invention, the step of obtaining the degree of interference includes: calculating the average value of the time interval between the abnormal peak point and the adjacent abnormal peak point to obtain the interval characteristic value, the adjacent abnormal peak points are the two abnormal peak points closest to the abnormal peak point before and after the abnormal peak point, when there is only one adjacent abnormal peak point, directly take the time interval between the abnormal peak point and the adjacent abnormal peak point as the interval characteristic value; when the interval characteristic value is smaller, it means that the current fluctuation is more frequent, the more dense the abnormal peak points appear, the more likely it is that the current fluctuation is caused by temperature regulation after the temperature anomaly. For any data point between the abnormal peak point and the adjacent trough point, calculate the absolute value of the difference between the tangent slope of the arbitrary data point and the adjacent data point to obtain the change characteristic value; in an embodiment of the present invention, the adjacent data point is the data point at the previous moment of the arbitrary data point. Calculate the sum of the change characteristic values ​​of all data points between the abnormal peak point and the adjacent trough point to obtain the cumulative change characteristic value; when the cumulative change characteristic value is smaller, it means that the data change between the abnormal peak point and the adjacent trough point is more gradual, and it is more likely that the current fluctuation occurs after temperature control; when the cumulative change characteristic value is larger, it means that the data change is more drastic, and it is more likely that it is an instantaneous large fluctuation of the current under normal circumstances. Calculate the product of the reciprocal of the interval characteristic value and the reciprocal of the cumulative change characteristic value to obtain the degree of interference of the abnormal peak point; when the interval characteristic value and the cumulative change characteristic value are smaller, the greater the degree of interference, and it is more likely that the current fluctuation is caused by the temperature abnormality caused by the battery itself or the outside world, and the more likely it is to affect the coulomb efficiency. The formulas for obtaining the degree of interference include:

[0042]

[0043] In the formula, R represents the degree of interference of the abnormal peak point, T represents the interval characteristic value, and N represents the number of data points between the abnormal peak point and the adjacent trough point. represents the changing characteristic value of the nth data point, Indicates the cumulative change characteristic value.

[0044] After obtaining the interference degree of different abnormal peak points, the current abnormal point can be determined according to the interference degree of the abnormal peak point; preferably, in the embodiment of the present invention, the step of obtaining the current abnormal point includes: taking the abnormal peak point whose interference degree exceeds the preset interference threshold as the current abnormal point; in the embodiment of the present invention, the preset interference threshold is 0.4, which can be determined by the implementer according to the implementation scenario; the current abnormal point characterizes the time period when the current fluctuates abnormally due to abnormal interference.

[0045] Step S3, obtaining a current abnormal change sequence according to the data difference characteristics between adjacent current abnormal points; obtaining a temperature change feature sequence according to the difference characteristics between the temperature data at the same time as the current abnormal point in the temperature curve; obtaining a similarity degree according to the change similarity characteristics between the current abnormal change sequence and the temperature change feature sequence.

[0046] During the charging and discharging process of the battery, the voltage will change due to the change of the state of charge value. The voltage fluctuation in such a battery will also cause the current to fluctuate. Therefore, some current abnormal points may be caused by voltage fluctuations, and such normal voltage fluctuations will not affect the coulomb efficiency. Therefore, it is necessary to further analyze the correlation between the current abnormal points and the temperature abnormalities, so as to improve the accuracy of the state of charge value calculation. First, the current abnormal change sequence is obtained according to the data difference characteristics between adjacent current abnormal points; preferably, in an embodiment of the present invention, the step of obtaining the current abnormal change sequence includes: calculating the numerical difference between any current abnormal point and the current abnormal point at the adjacent historical moment, obtaining the adjacent difference value of the arbitrary current abnormal point, and the adjacent historical moment is the moment before the moment of the arbitrary current abnormal point; sorting the adjacent difference values ​​of all current abnormal points in chronological order to obtain the current abnormal change sequence; the current abnormal change sequence reflects the abnormal change characteristics of the current. Furthermore, a temperature change characteristic sequence is obtained based on the difference characteristics between the temperature data at the same time as the current anomaly point in the temperature curve diagram; preferably, in an embodiment of the present invention, the step of obtaining the temperature change characteristic sequence includes: taking the temperature at the time when the current anomaly point is located in the temperature curve diagram as the marked temperature; calculating the difference between any marked temperature and the marked temperature at adjacent historical moments to obtain a temperature difference value; sorting the temperature difference values ​​of all marked temperatures in actual order to obtain a temperature change characteristic sequence.

[0047] If the changing characteristics of the current are correlated with the changing characteristics of the temperature, it means that the temperature anomaly is more likely to cause the current anomaly point to appear, so the similarity is obtained according to the similarity characteristics of the current anomaly change sequence and the temperature change characteristic sequence; preferably, in the embodiment of the present invention, the step of obtaining the similarity includes: calculating the absolute value of the Pearson correlation coefficient of the current anomaly change sequence and the temperature change characteristic sequence to obtain the similarity; it should be noted that the Pearson correlation coefficient belongs to the prior art, and the specific calculation steps are not repeated, and the value range of the Pearson correlation coefficient is [-1,1]. When the similarity is closer to 1, it means that the correlation between the current anomaly change sequence and the temperature change characteristic sequence is stronger, and the possibility of the temperature anomaly causing the current anomaly point to appear is greater; when the similarity is closer to 0, it means that the correlation between the current anomaly change sequence and the temperature change characteristic sequence is weaker, and the possibility of the voltage change causing the current anomaly point to appear is greater.

[0048] Step S4, obtaining an adjustment coefficient according to the degree of similarity, the distance characteristics of the current anomaly point closest to the current moment, and the degree of interference; adjusting the preset coulomb efficiency according to the adjustment coefficient to obtain an adaptive coulomb efficiency; obtaining the state of charge value at the next moment by the ampere-hour integration method according to the state of charge value at the current moment and the adaptive coulomb efficiency.

[0049] When the similarity and the degree of interference of the current abnormal point are large, it means that the temperature abnormality has affected the normal charging and discharging process and coulomb efficiency of the battery; the more abnormal the temperature is, the lower the coulomb efficiency will be, and the accuracy of calculating the state of charge based on the fixed coulomb efficiency will be lower when using the existing ampere-hour integration method. In order to improve the accuracy of the state of charge calculation, the coulomb efficiency can be adaptively adjusted according to the temperature abnormality, so the adjustment coefficient is obtained according to the similarity, the distance characteristics of the current abnormal point closest to the current moment, and the degree of interference.

[0050] Preferably, in an embodiment of the present invention, the step of obtaining the adjustment coefficient includes: calculating the average value of the time intervals of all adjacent current anomaly points to obtain a time length value; the time length value represents the average interval time length of adjacent current anomaly points. If there is no current anomaly point in the historical time range of the time length value adjacent to the current moment, the adjustment coefficient at the current moment is a constant 1; this means that there is no temperature anomaly affecting the charging and discharging process of the battery in the adjacent historical period at the current moment, so there is no need to adjust the coulomb efficiency. If there is a current anomaly point in the historical time range of the time length value adjacent to the current moment, this means that there is a temperature anomaly affecting the charging and discharging process of the battery in the adjacent historical period at the current moment, and the coulomb efficiency will be reduced, so it needs to be adjusted. Calculate the product of the interference degree and the similarity degree of the current anomaly point closest to the current moment to obtain the temperature influence characteristic value; when the interference degree and the similarity degree are greater, it means that the temperature influence characteristic value is greater, the temperature anomaly affects the charging and discharging process more, and the coulomb efficiency is lower. Calculate the inverse of the time interval between the current moment and the current anomaly point closest to the current moment to obtain the distance weight; when the distance weight is larger, it means that the current anomaly point is closer to the current moment, and the current moment's charging and discharging is more susceptible to the impact of temperature anomalies. Calculate the product of the temperature impact characteristic value, the distance weight, and the preset adjustment reference to obtain the adjustment degree; in an embodiment of the present invention, the preset adjustment reference is 0.2, and the implementer can determine it according to the implementation scenario; when the adjustment degree is larger, it means that the temperature anomaly affects the charging and discharging process more, and the coulomb efficiency should be lower. Calculate the difference between the constant 1 and the adjustment degree to obtain the adjustment coefficient; when the adjustment coefficient is smaller, it means that the coulomb efficiency should be lower; the formula for obtaining the adjustment coefficient includes:

[0051]

[0052] In the formula, H represents the adjustment coefficient, a represents the preset adjustment reference, and L represents the time interval between the current abnormal point at the current moment and the current abnormal point closest to the current moment. represents the distance weight, D represents the similarity, R represents the interference degree, Indicates that temperature affects the characteristic value; Indicates the degree of adjustment.

[0053] Further, the preset coulombic efficiency can be adjusted according to the adjustment coefficient to obtain the adaptive coulombic efficiency, specifically including: calculating the product of the adjustment coefficient and the preset coulombic efficiency to obtain the adaptive coulombic efficiency; when the adjustment coefficient is smaller, it means that the temperature is more abnormal, the charging and discharging process of the battery is more seriously affected, and the adaptive coulombic efficiency is smaller; it should be noted that the preset coulombic efficiency is determined by the implementer according to the characteristics of the battery, and is not limited here. After obtaining the adaptive coulombic efficiency at the current moment, the state of charge value at the next moment can be obtained by the ampere-hour integration method according to the state of charge value at the current moment and the adaptive coulombic efficiency; it should be noted that the ampere-hour integration method belongs to the prior art, and the specific calculation steps of the state of charge are no longer repeated. So far, the degree of influence of abnormal temperature on the battery is determined by analyzing the fluctuation characteristics of the current, and the coulombic efficiency is adaptively adjusted according to the degree of temperature influence, thereby improving the accuracy of the calculation of the state of charge of the battery.

[0054] In summary, the embodiment of the present invention provides a method for assessing the state of charge of a digital power grid; the abnormal peak point is determined according to the data fluctuation characteristics of the peak point in the current curve; the interference degree and the current abnormal point are obtained according to the distance characteristics of the abnormal peak point and the data change characteristics between the abnormal peak point and the adjacent trough point; the current abnormal change sequence is obtained according to the data difference between adjacent current abnormal points; the temperature change characteristic sequence is obtained according to the temperature curve; the similarity is obtained according to the current abnormal change sequence and the temperature change characteristic sequence; the adjustment coefficient is obtained according to the similarity, the distance characteristics of the current abnormal point closest to the current moment, and the interference degree. The present invention adjusts the preset coulomb efficiency according to the adjustment coefficient to obtain the adaptive coulomb efficiency and calculates the state of charge value at the next moment, thereby improving the calculation accuracy of the state of charge.

[0055] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for assessing the state of charge of a digital power grid, characterized in that: The method comprises the following steps: Obtain the current state of charge value of the battery, the current curve graph and the temperature curve graph of the recent historical period; Obtain an abnormal characteristic value according to the data fluctuation characteristics of the peak point in the current curve; obtain an abnormal peak point according to the abnormal characteristic value of the peak point; obtain the interference degree according to the distance characteristics between the abnormal peak point and the adjacent abnormal peak point, and the data change characteristics between the abnormal peak point and the adjacent trough point; determine the current abnormal point according to the interference degree of the abnormal peak point; Obtain a current abnormal change sequence according to the data difference characteristics between adjacent current abnormal points; obtain a temperature change feature sequence according to the difference characteristics between the temperature data at the same time as the current abnormal point in the temperature curve; obtain a similarity degree according to the change similarity characteristics between the current abnormal change sequence and the temperature change feature sequence; An adjustment coefficient is obtained according to the similarity, the distance characteristics of the current anomaly point closest to the current moment and the interference degree; the preset coulomb efficiency is adjusted according to the adjustment coefficient to obtain an adaptive coulomb efficiency; and the charge state value at the next moment is obtained by the ampere-hour integration method according to the charge state value at the current moment and the adaptive coulomb efficiency.

2. A method for assessing the state of charge of a digital power grid according to claim 1, characterized in that: The step of obtaining an abnormal characteristic value according to the data fluctuation characteristics of the peak point in the current curve graph comprises: The average value of the numerical difference between the peak point and adjacent trough points at the previous and next moments is calculated and normalized to obtain the abnormal characteristic value of the peak point.

3. A method for assessing the state of charge of a digital power grid according to claim 1, characterized in that: The step of obtaining an abnormal peak point according to the abnormal characteristic value of the peak point comprises: The peak point where the abnormal characteristic value exceeds the preset abnormal threshold is taken as the abnormal peak point.

4. A method for assessing the state of charge of a digital power grid according to claim 1, characterized in that: The step of obtaining the interference degree according to the distance characteristics between the abnormal peak point and the adjacent abnormal peak point and the data change characteristics between the abnormal peak point and the adjacent trough point comprises: Calculate the average value of the time interval between the abnormal peak point and the adjacent abnormal peak point to obtain the interval characteristic value; for any data point between the abnormal peak point and the adjacent trough point, calculate the absolute value of the difference between the tangent slope of the arbitrary data point and the adjacent data point to obtain the change characteristic value; calculate the sum of the change characteristic values ​​of all data points between the abnormal peak point and the adjacent trough point to obtain the cumulative change characteristic value; calculate the product of the reciprocal of the interval characteristic value and the reciprocal of the cumulative change characteristic value to obtain the interference degree of the abnormal peak point.

5. A method for assessing the state of charge of a digital power grid according to claim 1, characterized in that: The step of determining the abnormal current point according to the interference degree of the abnormal wave peak point comprises: The abnormal peak point whose interference degree exceeds a preset interference threshold is taken as the current abnormal point.

6. A method for assessing the state of charge of a digital power grid according to claim 1, characterized in that: The step of obtaining the current abnormal change sequence according to the data difference characteristics between adjacent current abnormal points includes: The numerical difference between any current anomaly point and the current anomaly point at adjacent historical moments is calculated to obtain adjacent difference values ​​of the any current anomaly point, and the adjacent difference values ​​of all current anomaly points are sorted in chronological order to obtain the current anomaly change sequence.

7. A method for assessing the state of charge of a digital power grid according to claim 1, characterized in that: The step of obtaining a temperature change characteristic sequence according to the difference characteristics between the temperature data at the same time as the current abnormal point in the temperature curve graph comprises: In the temperature curve diagram, the temperature at the time when the current abnormal point is located is used as the marked temperature; the difference between any marked temperature and the marked temperature at the adjacent historical moment is calculated to obtain the temperature difference value; the temperature difference values ​​of all marked temperatures are sorted in actual order to obtain the temperature change characteristic sequence.

8. A method for assessing the state of charge of a digital power grid according to claim 1, characterized in that: The step of obtaining the similarity degree according to the similarity characteristics of the abnormal current change sequence and the temperature change characteristic sequence comprises: The absolute value of the Pearson correlation coefficient between the abnormal current change sequence and the temperature change characteristic sequence is calculated to obtain the similarity degree.

9. A method for assessing state of charge of a digital power grid according to claim 1, characterized in that: The step of obtaining the adjustment coefficient according to the similarity, the distance characteristic of the current abnormal point closest to the current moment and the interference degree comprises: Calculate the average of the time intervals of all adjacent current abnormal points to obtain a time length value; if there is no current abnormal point within the historical time range of the time length value adjacent to the current moment, the adjustment coefficient of the current moment is a constant of 1; If there is a current anomaly point within the historical time range of the time length value adjacent to the current moment, calculate the product of the interference degree and the similarity degree of the current anomaly point closest to the current moment to obtain the temperature influence characteristic value; calculate the inverse of the time interval between the current moment and the current anomaly point closest to the current moment to obtain the distance weight; calculate the product of the temperature influence characteristic value, the distance weight, and the preset adjustment reference to obtain the adjustment degree; calculate the difference between the constant 1 and the adjustment degree to obtain the adjustment coefficient.

10. A method for assessing state of charge of a digital power grid according to claim 1, characterized in that: The step of adjusting the preset coulombic efficiency according to the adjustment coefficient to obtain the adaptive coulombic efficiency comprises: The product of the adjustment coefficient and the preset coulombic efficiency is calculated to obtain the adaptive coulombic efficiency.

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