An intelligent detection method for hidden dangers of combined communication power supply in substation

Abstract

The present invention relates to the field of battery charging technology, and specifically to an intelligent detection method for hidden dangers of combined communication power supply in a substation, comprising: obtaining a battery temperature time series data sequence and an ambient temperature time series data sequence of each charging process of the battery, obtaining a temperature fluctuation index of each time series data sequence segment, thereby obtaining the heat dissipation effect of the time period corresponding to each time series data sequence segment, and then obtaining the degree to which the battery meets the characteristics of the heat accumulation state in the time period corresponding to each time series data sequence segment, so as to screen out the thermal runaway critical value of each charging process of the battery from the battery temperature time series data sequence, thereby obtaining the performance attenuation of the current charging process of the battery. The present invention determines an accurate thermal runaway critical value by respectively determining the ambient temperature and charging temperature of different time periods, thereby analyzing the performance attenuation of the battery and realizing the hidden danger detection of the communication power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of battery charging, and in particular to an intelligent detection method for hidden dangers of combined communication power supply in a substation. Background Art

[0002] Currently, most communication power accidents are caused by battery failures and missed monitoring. Therefore, the maintenance and fault detection of battery packs in communication power systems are becoming increasingly important. Thermal runaway is one of the common battery failures. During the charging process, the internal battery temperature rises due to the increase in external ambient temperature and the chemical reaction inside the battery. When the battery temperature reaches a critical state, the heat inside the battery accumulates repeatedly. If the corresponding heat dissipation measures are not taken to cool the battery at this time, the battery temperature will further rise until it reaches the critical temperature value of thermal runaway. Thermal runaway will occur, causing the battery to leak gas, the shell to swell, and even cause an explosion, resulting in the complete shutdown of the communication power supply.

[0003] Existing problems: Current measures for preventing and controlling thermal runaway failures include adjusting the overall ambient temperature, enhancing the ventilation capacity of the base station, or setting alarm temperature thresholds through online monitoring technology. However, as the battery usage time increases, the risk of thermal runaway will increase, and the thermal runaway threshold will decrease. At this time, a single, unchanging threshold cannot accurately determine the risk of thermal runaway. Summary of the invention

[0004] The present invention provides a method for intelligently detecting hidden dangers of a combined communication power supply in a transformer substation, so as to solve the existing problems.

[0005] The present invention provides a method for intelligently detecting hidden dangers of a combined communication power supply for a substation using the following technical solutions:

[0006] An embodiment of the present invention provides a method for intelligently detecting hidden dangers of a combined communication power supply in a substation, the method comprising the following steps:

[0007] Obtaining a battery temperature time series data sequence and an ambient temperature time series data sequence during each battery charging process;

[0008] The battery temperature time series data sequence and the ambient temperature time series data sequence are respectively divided into a plurality of time series data sequence segments; according to the time length and temperature difference of the time series data sequence segments in the battery temperature time series data sequence and the ambient temperature time series data sequence, a temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence is obtained;

[0009] According to the temperature fluctuation index and temperature difference of each time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence, the heat dissipation effect of each time series data sequence segment in the battery temperature time series data sequence is obtained;

[0010] According to the heat dissipation effect, duration and temperature difference of each time series data sequence segment in the battery temperature time series data sequence, the degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state is obtained; according to the degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state, a number of time series data sequence segments in the heat accumulation state are screened out;

[0011] According to the difference in the degree to which the battery meets the characteristics of the heat accumulation state in the time period corresponding to the time series data sequence segment in the heat accumulation state, the thermal runaway critical value of each charging process of the battery is screened out from the battery temperature time series data sequence; according to the thermal runaway critical value of each charging process of the battery, the performance degradation of the current charging process of the battery is obtained.

[0012] Furthermore, the battery temperature time series data sequence and the ambient temperature time series data sequence are divided into a plurality of time series data sequence segments, including the following specific steps:

[0013] During each battery charging process, the battery temperature time series data sequence is divided into several time series data sequence segments using the APCA segmentation method; the ambient temperature time series data sequence is divided into several time series data sequence segments using the start and end time of each time series data sequence segment divided by the battery temperature time series data sequence.

[0014] Furthermore, the temperature fluctuation index of each time series data segment in the battery temperature time series data sequence and the ambient temperature time series data sequence includes the following specific steps:

[0015] Obtain the APCA approximate temperature value of each time series data sequence segment divided by the battery temperature time series data sequence;

[0016] In the battery temperature time series data sequence of each battery charging process, the difference between the average duration of all time series data sequence segments and the duration of each time series data sequence segment is calculated, the difference between the average APCA approximate temperature value of all time series data sequence segments and the APCA approximate temperature value of each time series data sequence segment is calculated, and the product of the variance of all battery temperatures in each time series data sequence segment, the difference in the duration, and the difference in the APCA approximate temperature value is recorded as the temperature fluctuation index of each time series data sequence segment;

[0017] According to the method of obtaining the temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence, the temperature fluctuation index of each time series data sequence segment in the ambient temperature time series data sequence is obtained.

[0018] Furthermore, the heat dissipation effect of the time period corresponding to each time series data sequence segment in the battery temperature time series data sequence includes the following specific steps:

[0019] In each battery charging process, the temperature characteristics of each time series data sequence segment are obtained according to the average battery temperature and the average ambient temperature in the time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence;

[0020] Calculate the sum of the differences between the battery temperature and the ambient temperature of all the battery temperatures with the same ordinal value in each time series data sequence segment in the battery temperature time series data sequence and the ambient battery temperature time series data sequence, calculate the difference in the temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence and the ambient battery temperature time series data sequence, and record the product of the sum and the difference in the temperature fluctuation index as the ambient charging temperature characteristic difference of each time series data sequence segment;

[0021] The product of the temperature characteristic and the difference between the ambient charging temperature characteristic is recorded as the heat dissipation effect of the time period corresponding to each time series data sequence segment in the battery temperature time series data sequence.

[0022] Furthermore, the temperature characteristics of each time series data sequence segment include the following specific steps:

[0023] Calculate the battery temperature time series data sequence The ratio of the average battery temperature in the time series data segment to the average battery temperature in the battery temperature time series data sequence is calculated. The normalized value of the difference between the average ambient temperature in the first time series data segment and the average ambient temperature in the second time series data segment, and the product of the ratio and the normalized value is recorded as the first Temperature characteristics of a time series data segment.

[0024] Furthermore, the degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state includes the following specific steps:

[0025] In the battery temperature time series data sequence of each battery charging process, calculate the duration of the battery temperature time series data sequence and the The ratio of the duration of the time series data segments is calculated. and The difference in the average battery temperature of the time series data segments is calculated. The inverse proportional value of the ratio of the heat dissipation effect of the time period corresponding to the time series data sequence segment to the average heat dissipation effect of the time period corresponding to all time series data sequence segments is obtained, and the normalized value of the product of the ratio of the time lengths, the difference and the inverse proportional value is recorded as The degree to which the battery meets the characteristics of the heat accumulation state during the period corresponding to each time series data segment.

[0026] Furthermore, the step of selecting a plurality of time series data segments in a heat accumulation state includes the following specific steps:

[0027] In the battery temperature time series data sequence of each battery charging process, the time series data sequence segment in which the degree to which the battery meets the heat accumulation state characteristics during the corresponding period is greater than a preset judgment threshold is recorded as the time series data sequence segment in the heat accumulation state.

[0028] Furthermore, the thermal runaway critical value of each charging process of the battery includes the following specific steps:

[0029] In the battery temperature time series data sequence of each battery charging process, The front and rear of each segment of the time series data in the heat accumulation state The time series data sequence segments in the heat accumulation state are recorded as the reference time series data sequence segments in the heat accumulation state; wherein, is a preset quantity threshold;

[0030] Calculate the The average of the differences between the time series data segments in the heat accumulation state and the time periods corresponding to the reference time series data segments in the heat accumulation state to which the battery conforms to the characteristics of the heat accumulation state is calculated. The ratio of the degree to which the battery meets the characteristics of the heat accumulation state during the time period corresponding to the time series data sequence segment in the heat accumulation state to the mean value is recorded as The degree to which the time period corresponding to the time series data sequence segment in the heat accumulation state conforms to the thermal runaway characteristics;

[0031] The thermal runaway critical value is obtained according to the degree to which the time period corresponding to each time series data sequence segment in the heat accumulation state meets the thermal runaway characteristics.

[0032] Furthermore, the step of obtaining the thermal runaway critical value according to the degree to which the time period corresponding to each time series data sequence segment in the heat accumulation state meets the thermal runaway characteristics includes the following specific steps:

[0033] Record the time series data sequence segment in the heat accumulation state whose degree of conformity with the thermal runaway characteristics in the corresponding time period is greater than the preset judgment threshold as the target time series data sequence segment;

[0034] The minimum charging temperature in all target time series data sequence segments is recorded as the thermal runaway critical value.

[0035] Furthermore, the performance degradation of the battery during the current charging process includes the following specific steps:

[0036] During the current charging process of the battery, the ratio of the duration of the battery temperature time series data sequence to the time interval between the first battery temperature in the battery temperature time series data sequence and the thermal runaway critical value is calculated, the ratio of the difference between the thermal runaway critical value of the current charging process of the battery and the thermal runaway critical value of the first charging process of the battery to the thermal runaway critical value of the current charging process of the battery is calculated, and the product of the ratio of the time intervals and the ratio of the thermal runaway critical values ​​is recorded as the performance degradation of the battery in the current charging process.

[0037] The beneficial effects of the technical solution of the present invention are:

[0038] In the embodiment of the present invention, the battery temperature time series data sequence and the ambient temperature time series data sequence of each charging process of the battery are obtained, and the temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence is obtained, so as to obtain the heat dissipation effect of the time period corresponding to each time series data sequence segment, and thus the thermal runaway critical value is screened out from the charging thermal runaway period according to the heat dissipation effect, so as to ensure the accuracy of the hidden danger detection of the communication power supply. Then, the degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state is obtained, so as to screen out the thermal runaway critical value of each charging process of the battery from the battery temperature time series data sequence, and combine the analysis of the heat accumulation state when the battery is charged, so as to further ensure the accuracy of the thermal runaway critical value screened out from the charging thermal runaway period, so as to use the accurate change of the thermal runaway critical value to obtain the reliable performance attenuation of the current charging process of the battery. So far, the present invention determines the accurate thermal runaway critical value by respectively determining the ambient temperature and charging temperature in different time periods, so as to accurately analyze the performance attenuation of the battery and improve the accuracy of the hidden danger detection of the communication power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be 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.

[0040] Figure 1 This is a flowchart of the steps of a method for intelligently detecting hidden dangers of a combined communication power supply for a substation according to the present invention;

[0041] Figure 2This is a flow chart for obtaining the performance degradation of the battery during the current charging process in this embodiment. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, features and effects of a substation combined communication power supply hidden danger intelligent detection method proposed by the present invention are described in detail below 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.

[0043] 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.

[0044] The specific scheme of the intelligent detection method for hidden dangers of combined communication power supply of substation provided by the present invention is described in detail below with reference to the accompanying drawings.

[0045] See also Figure 1 , which shows a flowchart of a method for intelligently detecting hidden dangers of a combined communication power supply for a substation provided by an embodiment of the present invention, the method comprising the following steps:

[0046] Step S001: Obtaining a battery temperature time series data sequence and an ambient temperature time series data sequence during each battery charging process.

[0047] The purpose of this embodiment is to accurately determine the critical value of thermal runaway of the battery under different states, obtain the degree of attenuation of battery performance according to the difference in the critical value of thermal runaway, and enable the staff to maintain the battery in time. The present invention obtains the critical value of thermal runaway through the difference in the heat dissipation capacity of the battery at different times and the difference in the thermal runaway state of the battery under different usage levels, and sets an alarm prompt to ensure the safe operation of the communication power supply.

[0048] At present, the batteries commonly used in communication power supply systems are lead-acid batteries. The charging process of lead-acid batteries is an exothermic process, which will cause the power supply temperature to rise. The continuous accumulation of temperature will cause thermal runaway. Thermal runaway is one of the common faults of batteries. Therefore, this embodiment takes lead-acid batteries as an example, analyzes the thermal runaway state of the battery according to the change in battery temperature during the charging process, and analyzes the degree of attenuation of battery performance according to the difference in thermal runaway state of the battery at different usage levels.

[0049] This embodiment first measures the ambient temperature and battery temperature of a single battery of any communication base station. Note that temperature detection probes are installed in the environment where the battery is located and on the surface of the battery, and the temperature change of the battery during each charging time is collected to obtain the battery temperature time series data sequence and the ambient temperature time series data sequence of each charging process of the battery. The data is collected every 10 minutes, and this example is used for description.

[0050] Step S002: Divide the battery temperature time series data sequence and the ambient temperature time series data sequence into a number of time series data sequence segments respectively; obtain the temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence according to the duration and temperature difference of the time series data sequence segments in the battery temperature time series data sequence and the ambient temperature time series data sequence.

[0051] It is known that the critical value of battery thermal runaway varies depending on the heat dissipation conditions of the external environment and the length of battery use. Therefore, the degree of battery performance attenuation can be analyzed by analyzing the differences in the degree of heat dissipation and heat accumulation of the battery at different periods.

[0052] Since the lead-acid battery itself is a sealed device, the heat generation and heat dissipation inside the battery are unbalanced due to the influence of the external ambient temperature and the heat release of the chemical reaction inside the battery during the charging process, which makes the battery temperature change uneven and the obtained temperature data will also fluctuate. Therefore, in order to obtain the changing trend of the battery temperature in different time periods, the APCA segmentation method is used to segment the battery temperature time series data sequence to obtain multiple sequence segments with similar temperature changes, and the fluctuation index of each sequence segment is calculated.

[0053] Battery Taking the charging process as an example, the APCA segmentation method is used to divide the battery temperature time series data sequence to obtain several time series data sequence segments and the APCA approximate temperature value of each time series data sequence segment.

[0054] It should be noted that the APCA segmentation method is a well-known technology. In the APCA segmentation process, the data sequence is divided into several segments, and the data value of each segment can be approximated by a constant, which is the APCA approximate data value of each segment.

[0055] In each battery charging process, the ambient temperature time series data sequence is divided into several time series data sequence segments using the start and end time of each time series data sequence segment divided by the battery temperature time series data sequence. That is, the time series data sequence segments with the same ordinal values ​​of the battery temperature time series data sequence and the ambient temperature time series data sequence are in the same time period.

[0056] Preferably, in one embodiment of the present invention, a method for acquiring a temperature fluctuation index of each time series data sequence segment includes:

[0057] In the battery In the battery temperature time series data sequence of the first charging process, the difference between the average duration of all time series data sequence segments and the duration of each time series data sequence segment is calculated, and then the difference between the average APCA approximate temperature value of all time series data sequence segments and the APCA approximate temperature value of each time series data sequence segment is calculated. The product of the variance of all battery temperatures in each time series data sequence segment, the difference in the duration, and the difference in the APCA approximate temperature value is recorded as the temperature fluctuation index of each time series data sequence segment.

[0058] It should be noted that the duration of each time series data segment is the time interval between the first and last battery temperature corresponding times in the time series data segment. The specific calculation formula for the temperature fluctuation index of each time series data segment in this embodiment is:

[0059]

[0060] In the formula, For battery The battery temperature time series data sequence of the charging process The temperature fluctuation index of the time series data segment, For battery The battery temperature time series data sequence of the charging process The variance of all battery temperatures in a time series data segment, For battery The battery temperature time series data sequence of the charging process The duration of a time series data segment, For battery The average duration of all time series data sequence segments in the battery temperature time series data sequence of the charging process, For battery The battery temperature time series data sequence of the charging process APCA approximate temperature value of the time series data segment, For battery The average APCA approximate temperature value of all time series data segments in the battery temperature time series data sequence of the charging process, is the absolute value function.

[0061] It is further necessary to explain that: The larger the value is, the more dramatic the charging temperature change is in this time series data segment. and The larger it is, the greater the difference in duration and charging temperature of the timing data sequence segment relative to other timing data sequence segments. That is, the greater the relative fluctuation of the timing data sequence segment in the sequence. Therefore, the product of the three is used to represent the temperature fluctuation index.

[0062] According to the method of obtaining the temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence of each battery charging process, the temperature fluctuation index of each time series data sequence segment in the ambient temperature time series data sequence of each battery charging process is obtained.

[0063] Step S003: obtaining the heat dissipation effect of the time period corresponding to each time series data sequence segment in the battery temperature time series data sequence according to the temperature fluctuation index and temperature difference of each time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence.

[0064] Since the battery temperature is affected by the external environment and internal chemical reactions, the temperature difference between the inside and outside of the battery is large, that is, the charging process is an exothermic process, so the temperature inside the battery is often higher than the ambient temperature. The reason for thermal runaway is that the repeated heat accumulation inside the battery causes the battery temperature to reach the critical temperature value of thermal runaway, which in turn causes thermal runaway. Therefore, the stronger the heat dissipation capacity of the working environment of the battery, the better the ability to improve the temperature, and the smaller the possibility of thermal runaway, and vice versa. During the thermal accumulation process, the greater the degree of heat accumulation, the higher the accumulation frequency, and the closer it is to the thermal runaway state.

[0065] Since changes in the internal temperature of the communication power supply will also affect the external ambient temperature, as the charging time increases, the heat around the battery will accumulate to a certain extent, affecting the heat dissipation effect. An environment with good heat dissipation effect can significantly reduce the battery temperature.

[0066] Therefore, the higher the battery temperature in the current period, the worse the ability of the current environment to alleviate the battery temperature rise, that is, the worse the heat dissipation effect. The greater the difference between the ambient temperature in the current period and the ambient temperature at the start time, the greater the impact of the heat generated by the battery during the charging process on the ambient temperature, and the worse the heat dissipation effect in the current period. The greater the difference between the ambient temperature and the battery temperature corresponding to the same period, the worse the heat dissipation effect in the current period. The heat dissipation effect is analyzed by the change characteristics of the battery temperature and the ambient temperature in the current period and the difference between the two time series data sequence segments. The worse the heat dissipation effect, the greater the possibility of thermal runaway, and the time series data sequence segments with poor heat dissipation effect are screened.

[0067] Preferably, in one embodiment of the present invention, a method for acquiring the heat dissipation effect of a time period corresponding to each time series data sequence segment includes:

[0068] In the battery During the first charging process, calculate the battery temperature time series data sequence The ratio of the average battery temperature in the time series data segment to the average battery temperature in the battery temperature time series data sequence is calculated. The normalized value of the difference between the average ambient temperature in the first time series data segment and the average ambient temperature in the second time series data segment is obtained, and the product of the ratio and the normalized value is recorded as the first Calculate the temperature characteristics of the battery temperature time series data segment and the ambient battery temperature time series data segment. The sum of the differences between the battery temperature and the ambient temperature of all the battery temperatures with the same sequence value in the time series data sequence segment is calculated. The difference of the temperature fluctuation index of the time series data sequence segment is calculated, and the product of the sum and the difference of the temperature fluctuation index is recorded as The product of the temperature characteristic and the difference in the ambient charging temperature characteristic is recorded as the first time series data segment in the battery temperature time series data sequence. The heat dissipation effect of the time period corresponding to each time series data segment.

[0069] It should be noted that: in this embodiment, the specific calculation formula corresponding to the heat dissipation effect of each time series data sequence segment in the battery temperature time series data sequence during each charging process of the battery is:

[0070]

[0071] In the formula, For battery The battery temperature time series data sequence of the charging process The heat dissipation effect of the time period corresponding to the time series data segment, For battery The battery temperature time series data sequence of the charging process The average battery temperature in the time series data segment, For battery The average battery temperature in the battery temperature time series data sequence of the charging process, For battery The ambient temperature time series data sequence of the charging process The average ambient temperature in a time series data segment, For battery The average ambient temperature in the first time series data segment in the ambient temperature time series data sequence of the charging process, For battery The battery temperature time series data sequence of the charging process The temperature fluctuation index of the time series data segment, For battery The ambient temperature time series data sequence of the charging process The temperature fluctuation index of the time series data segment, For battery The battery temperature time series data sequence of the charging process The first segment of the time series data sequence The battery temperature, For battery The ambient temperature time series data sequence of the charging process The first segment of the time series data sequence Ambient temperature, For battery The battery temperature time series data sequence of the charging process The number of battery temperatures within a time series data segment. is the absolute value function, It is a normalization function used to normalize data values ​​to between 0 and 1. is an exponential function with a natural constant as the base. To present the inverse proportional relationship and normalization processing, the implementer can set the inverse proportional function and normalization function according to the actual situation. For battery During the first charging process The temperature characteristics of the time series data segment, For battery During the first charging process The difference in ambient charging temperature characteristics for each time series data segment.

[0072] It is further necessary to explain that: The larger the value is, the higher the battery temperature is during the current period, which means the heat dissipation effect is poor. and The larger the value, the greater the difference between the ambient temperature and the battery temperature in the same period, that is, the worse the heat dissipation effect. The larger it is, the higher the current ambient temperature is, which will lead to heat dissipation difficulties. Therefore, the product of the four is used to represent the heat dissipation effect.

[0073] Step S004: According to the heat dissipation effect, duration and temperature difference of the time period corresponding to each time series data sequence segment in the battery temperature time series data sequence, the degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state is obtained; according to the degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state, a number of time series data sequence segments in the heat accumulation state are screened out.

[0074] When approaching the critical value of thermal runaway, the temperature change inside the battery is in a state of repeated heat accumulation, that is, when the battery temperature rises rapidly, the internal resistance of the battery decreases, resulting in an increase in the charging current, and the increase in current will cause the battery temperature to rise, thus forming a cycle, resulting in repeated accumulation of heat. Therefore, the differences between the time series data sequence segments are analyzed in combination with the heat dissipation degree obtained above, and then the heat accumulation degree of different time series data sequence segments and the possibility of thermal runaway in each time series data sequence segment are obtained, and based on this, the time series data sequence segments with a high possibility of thermal runaway are screened.

[0075] When the internal temperature of the battery is in a state of heat accumulation, the temperature rises faster and faster, which should be reflected in the time series data sequence segments as follows: the distribution of the time series data sequence segments becomes more and more dense, that is, the corresponding time length of the time series data sequence segments becomes shorter and shorter, the temperature rises faster and faster, and the heat dissipation effect becomes worse and worse.

[0076] Preferably, in one embodiment of the present invention, a method for obtaining the degree to which the battery in the time period corresponding to each time series data sequence segment complies with the heat accumulation state characteristics comprises:

[0077] In the battery In the battery temperature time series data sequence of the first charging process, calculate the duration of the battery temperature time series data sequence and the The ratio of the duration of the time series data segments is calculated. and The difference of the average battery temperature of the time series data sequence segment, and then calculate the The inverse proportional value of the ratio of the heat dissipation effect of the time period corresponding to the time series data sequence segment to the average heat dissipation effect of the time period corresponding to all time series data sequence segments is recorded as the normalized value of the product of the ratio, the difference and the inverse proportional value. The degree to which the battery meets the characteristics of the heat accumulation state during the period corresponding to each time series data segment.

[0078] It should be noted that: in this embodiment, the degree of heat accumulation state characteristics is not analyzed for the first time series data sequence segment. The specific calculation formula corresponding to the degree of the battery meeting the heat accumulation state characteristics in the time period corresponding to each time series data sequence segment in the battery temperature time series data sequence during each charging process of the battery is:

[0079]

[0080] In the formula, For battery The battery temperature time series data sequence of the charging process The degree to which the battery meets the characteristics of the heat accumulation state during the period corresponding to each time series data segment, For battery The battery temperature time series data sequence of the charging process The duration of a time series data segment, For battery The duration of the battery temperature time series data sequence during the charging process, For battery The battery temperature time series data sequence of the charging process The average battery temperature in the time series data segment, For battery The battery temperature time series data sequence of the charging process The average battery temperature in the time series data segment, For battery The battery temperature time series data sequence of the charging process The heat dissipation effect of the time period corresponding to the time series data segment, For battery The average heat dissipation effect of all time series data sequence segments in the battery temperature time series data sequence of the charging process. It is a linear normalization function used to normalize data values ​​to between 0 and 1. is an exponential function with a natural constant as the base. To present the inverse proportional relationship and normalization processing, the implementer can set the inverse proportional function and normalization function according to the actual situation.

[0081] It is further necessary to explain that: The smaller it is, the shorter the time series data segment is, and the more likely the battery is in a state of heat accumulation. The larger it is, the faster the heat accumulation is. The smaller the The worse the heat dissipation effect is during the period corresponding to the time series data segment, the better the heat accumulation effect is. Indicates battery The battery temperature time series data sequence of the charging process The degree to which the battery meets the characteristics of the heat accumulation state during the period corresponding to the time series data sequence segment.

[0082] Preferably, in one embodiment of the present invention, a method for acquiring a time series data sequence segment in a heat accumulation state includes:

[0083] The preset judgment threshold in this embodiment is 0.7, and the preset quantity threshold 3 is taken as an example. In the battery temperature time series data sequence of each charging process of the battery, the time series data sequence segment in which the degree to which the battery in the corresponding time period meets the characteristics of the heat accumulation state is greater than the preset judgment threshold is recorded as the time series data sequence segment in the heat accumulation state.

[0084] Step S005: According to the difference in the degree to which the battery in the time period corresponding to the time series data sequence segment in the heat accumulation state meets the characteristics of the heat accumulation state, the thermal runaway critical value of each charging process of the battery is screened out from the battery temperature time series data sequence; according to the thermal runaway critical value of each charging process of the battery, the performance degradation of the current charging process of the battery is obtained.

[0085] Since thermal runaway is the result of heat accumulation in the battery, the closer to the thermal runaway moment, the higher the probability that the corresponding period is in a thermal accumulation state, and the difference in the probability of being in a thermal accumulation state between different periods is small. Therefore, all time series data segments in a thermal accumulation state are traversed, and the difference in the thermal accumulation probability with the adjacent time series data segments is compared and analyzed to determine the thermal runaway critical value of the current battery.

[0086] Preferably, in one embodiment of the present invention, a method for obtaining the degree to which the time period corresponding to each time series data sequence segment in a heat accumulation state complies with the thermal runaway characteristics comprises:

[0087] In the battery In the battery temperature time series data sequence of the charging process, The front and rear of each segment of the time series data in the heat accumulation state The time series data sequence segment in the heat accumulation state is recorded as the reference time series data sequence segment in the heat accumulation state. The average of the differences between the time series data segments in the heat accumulation state and the time periods corresponding to the reference time series data segments in the heat accumulation state to which the battery conforms to the characteristics of the heat accumulation state is calculated. The ratio of the degree to which the battery meets the characteristics of the thermal accumulation state during the period corresponding to the time series data sequence segment in the thermal accumulation state to the mean value is recorded as The degree to which the time period corresponding to the time series data segment in the heat accumulation state conforms to the thermal runaway characteristics.

[0088] What needs to be explained is: There are not enough time series data on either side of the segment in the heat accumulation state. When there are time series data segments in the heat accumulation state, the existing time series data segments in the heat accumulation state are selected for subsequent analysis. The specific calculation formula corresponding to the degree to which the time period corresponding to each time series data segment in the heat accumulation state meets the thermal runaway characteristics is:

[0089]

[0090] In the formula, For battery The battery temperature time series data sequence of the charging process The degree to which the time period corresponding to the time series data segment in the heat accumulation state conforms to the characteristics of thermal runaway, For battery The battery temperature time series data sequence of the charging process The degree to which the battery in the time period corresponding to the time series data sequence segment in the heat accumulation state meets the characteristics of the heat accumulation state, For battery The battery temperature time series data sequence of the charging process The first segment of the time series data sequence in the heat accumulation state The degree to which the battery in the period corresponding to the reference time series data sequence segment in the heat accumulation state meets the characteristics of the heat accumulation state, For battery The battery temperature time series data sequence of the charging process The number of reference time series data sequence segments in the heat accumulation state of the time series data sequence segments in the heat accumulation state, is the absolute value function. It is a linear normalization function used to normalize data values ​​to between 0 and 1.

[0091] It is further necessary to explain that: The larger the value, the greater the possibility of thermal runaway. The smaller it is, the more stable the heat accumulation state is within a period of time, and when the heat accumulation is saturated, it will tend to be stable. The normalized value of the battery The battery temperature time series data sequence of the charging process The degree to which the time period corresponding to the time series data segment in the heat accumulation state conforms to the thermal runaway characteristics.

[0092] Preferably, in one embodiment of the present invention, a method for obtaining a thermal runaway critical value of a battery during each charging process includes:

[0093] In the battery temperature time series data sequence of each battery charging process, the time series data sequence segment in the heat accumulation state whose degree of conformity with the thermal runaway characteristics is greater than the preset judgment threshold is recorded as the target time series data sequence segment. The minimum charging temperature in all target time series data sequence segments is recorded as the thermal runaway critical value.

[0094] It should be noted that: if there are multiple minimum charging temperatures, the minimum charging temperature closest to the start time of charging is taken as the thermal runaway critical value.

[0095] As the battery is used for a longer time, its internal water loss will increase, causing the electrolyte saturation to decrease, battery performance to decline, and the battery's thermal runaway threshold to decrease accordingly, making the risk of thermal runaway of the battery increasingly greater. Therefore, the degree of battery performance attenuation is obtained by analyzing the degree of decrease in the thermal runaway threshold of batteries with different usage times and the difference in the time when thermal runaway occurs.

[0096] The longer the usage time, that is, the more times the battery is charged, the greater the degree of battery performance degradation, the higher the degree of water loss inside the battery, the greater the degree of decrease in electrolyte saturation, the lower the critical temperature of battery thermal runaway will be, and the degree of decrease will become greater and greater, and the time of thermal runaway will occur earlier and earlier. Therefore, based on the above characteristics, the degree of battery performance degradation at different usage levels is analyzed.

[0097] Taking the first charging process of the battery as a reference standard, the performance degradation of the current charging process of the battery is analyzed according to the differences in the thermal runaway occurrence time and thermal runaway critical value between the current charging process of the battery and the first charging process.

[0098] Preferably, in one embodiment of the present invention, the method for obtaining the performance degradation of the current charging process of the battery includes:

[0099] In the battery temperature time series data sequence of the current charging process of the battery, the ratio of the duration of the battery temperature time series data sequence to the time interval between the first battery temperature in the battery temperature time series data sequence and the thermal runaway critical value is calculated, and then the ratio of the difference between the thermal runaway critical value of the current charging process of the battery and the thermal runaway critical value of the first charging process of the battery and the thermal runaway critical value of the current charging process of the battery is calculated, and the product of the ratio of the time intervals and the ratio of the thermal runaway critical values ​​is recorded as the performance degradation of the current charging process of the battery.

[0100] It should be noted that the specific calculation formula corresponding to the performance attenuation of the current charging process of the battery is:

[0101]

[0102] In the formula, is the performance degradation of the battery during the current charging process. is the duration of the battery temperature time series data sequence of the current charging process of the battery, is the time interval between the first battery temperature and the thermal runaway critical value in the battery temperature time series data sequence of the current charging process of the battery, is the critical value of thermal runaway during the first charging process of the battery, It is the thermal runaway critical value of the current charging process of the battery. It is a linear normalization function used to normalize data values ​​to between 0 and 1.

[0103] It is further necessary to explain that: The smaller it is, the earlier the battery will experience thermal runaway during the current charging process. The larger the value, the greater the degree of reduction in the critical value of thermal runaway during the current charging process. The larger the value is, the more serious the battery performance degradation is in the current state. Since the preset judgment threshold is used to obtain the thermal runaway critical value of each charging process of the battery, it is impossible to obtain the thermal runaway critical value for each charging process. Therefore, the first charging process and the current charging process in this embodiment are the first and last charging processes with thermal runaway critical values ​​selected in chronological order. Among them, the flow chart for obtaining the performance degradation of the current charging process of the battery is as follows: Figure 2 shown.

[0104] The thermal runaway critical value of the current charging process of the battery is used as the thermal runaway critical value of the next charging process in the future. When the charging temperature in the next charging process in the future is greater than the thermal runaway critical value, the battery warning information is immediately output to the staff, that is, the thermal runaway critical value of the current charging process of the battery and the performance degradation, so that the staff can perform timely maintenance and ensure the safe operation of the communication power supply.

[0105] It should be noted that: in this embodiment, when the denominator in the formula is 0, the denominator is set to 1, and this example is used for description to ensure that the formula is valid.

[0106] So far, the present invention is completed.

[0107] In summary, in the embodiment of the present invention, the battery temperature time series data sequence and the ambient temperature time series data sequence of each charging process of the battery are obtained, and the temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence is obtained, so as to obtain the heat dissipation effect of the time period corresponding to each time series data sequence segment in the battery temperature time series data sequence, and then obtain the degree to which the battery meets the characteristics of the heat accumulation state in the time period corresponding to each time series data sequence segment, so as to filter out the thermal runaway critical value of each charging process of the battery from the battery temperature time series data sequence, so as to obtain the performance attenuation of the current charging process of the battery. The present invention determines the accurate thermal runaway critical value by respectively determining the ambient temperature and charging temperature in different time periods, thereby analyzing the performance attenuation of the battery and realizing the hidden danger detection of the communication power supply.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent detection of hidden dangers of combined communication power supply in substation, characterized in that: The method comprises the following steps: Obtaining a battery temperature time series data sequence and an ambient temperature time series data sequence during each battery charging process; The battery temperature time series data sequence and the ambient temperature time series data sequence are respectively divided into a plurality of time series data sequence segments; according to the time length and temperature difference of the time series data sequence segments in the battery temperature time series data sequence and the ambient temperature time series data sequence, a temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence is obtained; According to the temperature fluctuation index and temperature difference of each time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence, the heat dissipation effect of each time series data sequence segment in the battery temperature time series data sequence is obtained; According to the heat dissipation effect, duration and temperature difference of each time series data sequence segment in the battery temperature time series data sequence, the degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state is obtained; according to the degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state, a number of time series data sequence segments in the heat accumulation state are screened out; According to the difference in the degree to which the battery meets the characteristics of the heat accumulation state in the time period corresponding to the time series data sequence segment in the heat accumulation state, the thermal runaway critical value of each charging process of the battery is screened out from the battery temperature time series data sequence; according to the thermal runaway critical value of each charging process of the battery, the performance degradation of the current charging process of the battery is obtained.

2. According to claim 1, a method for intelligent detection of hidden dangers of combined communication power supply in substations is characterized in that: The specific steps of dividing the battery temperature time series data sequence and the ambient temperature time series data sequence into a plurality of time series data sequence segments are as follows: During each battery charging process, the battery temperature time series data sequence is divided into several time series data sequence segments using the APCA segmentation method; the ambient temperature time series data sequence is divided into several time series data sequence segments using the start and end time of each time series data sequence segment divided by the battery temperature time series data sequence.

3. According to claim 1, a method for intelligent detection of hidden dangers of combined communication power supply in substations is characterized in that: The temperature fluctuation index of each time series data segment in the battery temperature time series data sequence and the ambient temperature time series data sequence includes the following specific steps: Obtain the APCA approximate temperature value of each time series data sequence segment divided by the battery temperature time series data sequence; In the battery temperature time series data sequence of each battery charging process, the difference between the average duration of all time series data sequence segments and the duration of each time series data sequence segment is calculated, the difference between the average APCA approximate temperature value of all time series data sequence segments and the APCA approximate temperature value of each time series data sequence segment is calculated, and the product of the variance of all battery temperatures in each time series data sequence segment, the difference in the duration, and the difference in the APCA approximate temperature value is recorded as the temperature fluctuation index of each time series data sequence segment; According to the method of obtaining the temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence, the temperature fluctuation index of each time series data sequence segment in the ambient temperature time series data sequence is obtained.

4. According to claim 1, a method for intelligently detecting hidden dangers of combined communication power supply in a substation is characterized in that: The heat dissipation effect of each time series data segment in the battery temperature time series data sequence corresponding to the time period includes the following specific steps: In each battery charging process, the temperature characteristics of each time series data sequence segment are obtained according to the average battery temperature and the average ambient temperature in the time series data sequence segment in the battery temperature time series data sequence and the ambient temperature time series data sequence; Calculate the sum of the differences between the battery temperature and the ambient temperature of all the battery temperatures with the same ordinal value in each time series data sequence segment in the battery temperature time series data sequence and the ambient battery temperature time series data sequence, calculate the difference in the temperature fluctuation index of each time series data sequence segment in the battery temperature time series data sequence and the ambient battery temperature time series data sequence, and record the product of the sum and the difference in the temperature fluctuation index as the ambient charging temperature characteristic difference of each time series data sequence segment; The product of the temperature characteristic and the difference between the ambient charging temperature characteristic is recorded as the heat dissipation effect of the time period corresponding to each time series data sequence segment in the battery temperature time series data sequence.

5. According to claim 4, a method for intelligent detection of hidden dangers of combined communication power supply in substations is characterized in that: The temperature characteristics of each time series data sequence segment include the following specific steps: Calculate the battery temperature time series data sequence The ratio of the average battery temperature in the time series data segment to the average battery temperature in the battery temperature time series data sequence is calculated. The normalized value of the difference between the average ambient temperature in the first time series data segment and the average ambient temperature in the second time series data segment, and the product of the ratio and the normalized value is recorded as the first Temperature characteristics of a time series data segment.

6. According to claim 1, a method for intelligent detection of hidden dangers of combined communication power supply in substations is characterized in that: The degree to which the battery in the time period corresponding to each time series data sequence segment meets the characteristics of the heat accumulation state includes the following specific steps: In the battery temperature time series data sequence of each battery charging process, calculate the duration of the battery temperature time series data sequence and the The ratio of the duration of the time series data segments is calculated. and The difference in the average battery temperature of the time series data segments is calculated. The inverse proportional value of the ratio of the heat dissipation effect of the time period corresponding to the time series data sequence segment to the average heat dissipation effect of the time period corresponding to all time series data sequence segments is obtained, and the normalized value of the product of the ratio of the time lengths, the difference and the inverse proportional value is recorded as The degree to which the battery meets the characteristics of the heat accumulation state during the period corresponding to the time series data sequence segment.

7. According to claim 1, a method for intelligently detecting hidden dangers of combined communication power supply in substations is characterized in that: The specific steps of screening out a plurality of time series data segments in a heat accumulation state include the following: In the battery temperature time series data sequence of each battery charging process, the time series data sequence segment in which the degree to which the battery meets the heat accumulation state characteristics during the corresponding period is greater than a preset judgment threshold is recorded as the time series data sequence segment in the heat accumulation state.

8. According to claim 1, a method for intelligently detecting hidden dangers of combined communication power supply in a substation, characterized in that: The critical value of thermal runaway of each battery charging process includes the following specific steps: In the battery temperature time series data sequence of each battery charging process, The front and rear of each segment of the time series data in the heat accumulation state The time series data sequence segments in the heat accumulation state are recorded as the reference time series data sequence segments in the heat accumulation state; wherein, is a preset quantity threshold; Calculate the The average of the differences between the time series data segments in the heat accumulation state and the time periods corresponding to the reference time series data segments in the heat accumulation state to which the battery conforms to the characteristics of the heat accumulation state is calculated. The ratio of the degree to which the battery meets the characteristics of the heat accumulation state during the time period corresponding to the time series data sequence segment in the heat accumulation state to the mean value is recorded as The degree to which the time period corresponding to the time series data segment in the heat accumulation state conforms to the characteristics of thermal runaway; The thermal runaway critical value is obtained according to the degree to which the time period corresponding to each time series data sequence segment in the heat accumulation state meets the thermal runaway characteristics.

9. According to claim 8, a method for intelligently detecting hidden dangers of combined communication power supply in a substation is characterized in that: The step of obtaining the thermal runaway critical value according to the degree to which the time period corresponding to each time series data sequence segment in the heat accumulation state meets the thermal runaway characteristics includes the following specific steps: Record the time series data sequence segment in the heat accumulation state whose degree of conformity with the thermal runaway characteristics in the corresponding time period is greater than the preset judgment threshold as the target time series data sequence segment; The minimum charging temperature in all target time series data sequence segments is recorded as the thermal runaway critical value.

10. According to claim 1, a method for intelligently detecting hidden dangers of combined communication power supply in a substation, characterized in that: The performance degradation of the battery during the current charging process includes the following specific steps: During the current charging process of the battery, the ratio of the duration of the battery temperature time series data sequence to the time interval between the first battery temperature in the battery temperature time series data sequence and the thermal runaway critical value is calculated, the ratio of the difference between the thermal runaway critical value of the current charging process of the battery and the thermal runaway critical value of the first charging process of the battery to the thermal runaway critical value of the current charging process of the battery is calculated, and the product of the ratio of the time intervals and the ratio of the thermal runaway critical values ​​is recorded as the performance degradation of the battery in the current charging process.

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