Fault detection early warning system of energy storage power supply
By analyzing the dynamic data of energy storage power sources, detection signals, suspected abnormal signals, and prediction signals are generated, which solves the shortcomings of energy storage power source fault early warning, realizes accurate identification and efficient early warning of early faults, and improves the operational safety and stability of the system.
Patent Information
- Application Number
- CN202511087162.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-05
- Publication Date
- 2025-11-21
AI Technical Summary
Existing technologies are unable to provide effective early warnings before energy storage power supplies malfunction or experience abnormalities, leading to system instability and reduced safety.
By acquiring dynamic data from energy storage power sources, analyzing parameters such as the proportion of unstable time, the proportion of ambient temperature deviation, temperature change value, the number of abnormal sub-curve groups, and overlapping characterization values, detection signals, suspected abnormal signals, and prediction signals are generated to achieve early fault warning and intelligent management.
It improves the safety and stability of energy storage power supply operation, enables accurate identification and efficient early warning of early faults, and enhances the accuracy and efficiency of system monitoring and maintenance.
Smart Images
Figure CN120993262A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of fault identification technology, specifically a fault detection and early warning system for energy storage power supplies. Background Technology
[0002] Energy storage power supplies are safe, portable, stable, and environmentally friendly small energy storage systems that can convert electrical energy into other forms of energy for storage, and then convert it back into electrical energy for output when needed. They use built-in high-energy-density lithium-ion batteries to provide a stable AC and DC power output. Energy storage power supplies are small in size and light in weight, and can be used for various functions such as balancing grid load, responding to grid fluctuations, providing backup power, storing renewable energy, and regulating voltage and frequency. They are widely used in outdoor activities and emergency disaster relief.
[0003] Energy storage power supply fault early warning refers to the process of detecting fault signs and sending early warning signals in advance by monitoring and analyzing system parameters and status before an energy storage power supply fails or becomes abnormal.
[0004] Therefore, the present invention provides a fault detection and early warning system for energy storage power supplies. Summary of the Invention
[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.
[0006] The technical solution adopted by the present invention to solve its technical problem is as follows: acquire dynamic data when the energy storage power supply is working, analyze the dynamic data to obtain the proportion of unstable time and the proportion of ambient temperature deviation, process the proportion of unstable time and the proportion of ambient temperature deviation to obtain the temperature change value, compare the temperature change value with the temperature change threshold, and generate a detection signal if the temperature change value is greater than the temperature change threshold. Based on the detection signal, the energy storage power supply detection data is obtained. Based on the detection data, the proportion of abnormal sub-curve groups and the relative temperature difference ratio are obtained. Based on the processing of the proportion of abnormal sub-curve groups and the relative temperature difference ratio, the curve performance value is obtained. The curve performance value is compared with the curve performance threshold. If the curve performance value is less than the curve performance threshold, a suspected abnormal signal is generated to prompt the staff to detect the abnormal period of the energy storage power supply. Based on suspected abnormal signals, overlapping data is acquired. The overlapping data is analyzed and processed to obtain the overlapping characterization value. Based on the comparison between the overlapping characterization value and the overlapping characterization threshold, if the overlapping characterization value is greater than or equal to the overlapping characterization threshold, an energy storage power prediction signal is generated. A further technical solution of the present invention is: the proportion of unstable time is marked as CZ, and the proportion of ambient temperature deviation is marked as PC; Through formula The temperature change value is calculated, where s1 and s2 are preset proportional coefficients; A further technical solution of the present invention is as follows: the method for obtaining the proportion of unstable time is: Obtain the ambient temperature value around the energy storage power supply, establish an XY coordinate system, where the X-axis represents the working time and the Y-axis represents the ambient temperature value, mark the temperature sensor temperature in the coordinate system according to the time sequence, and connect the marked points to obtain the ambient temperature change curve. In the ambient temperature change curve, a baseline is drawn based on the standard ambient temperature value; The non-steady time is obtained by statistically analyzing the operating time when the ambient temperature change curve of the energy storage power supply exceeds the baseline. The ratio of unstable time to total working time is calculated and processed to obtain the proportion of unstable time. A further technical solution of the present invention is as follows: the method for obtaining the percentage of ambient temperature deviation is: In the ambient temperature change curve, select the data points that exceed the baseline and connect them to obtain the unsteady temperature change curve; Several sampling points are selected in the unstable temperature change curve. The difference between the ambient temperature value of the sampling point and the standard ambient temperature value is calculated, and the absolute value is taken to obtain the ambient temperature deviation. The ratio of the ambient temperature deviation to the standard ambient temperature value is used to obtain the single temperature deviation ratio. The proportion of ambient temperature deviation is obtained by summing up all individual temperature deviation ratios and taking the average value. A further technical solution of the present invention is as follows: The relative temperature difference ratio JC and the proportion of abnormal sub-curve groups SL are processed using the formula... The curve performance value QX is calculated, where a1 and a2 are preset scaling coefficients; A further technical solution of the present invention is as follows: the method for obtaining the relative temperature difference ratio JC is as follows: The method for obtaining the relative temperature difference ratio JC is as follows: Based on the abnormal sub-curve group, the temperature difference value corresponding to the abnormal sub-curve group is processed with the temperature difference standard value to obtain the temperature relative difference of the abnormal sub-curve group. The temperature relative differences of all abnormal sub-curve groups are summed and averaged to obtain the temperature relative difference mean. The temperature relative difference mean is processed with the temperature difference standard value to obtain the temperature relative difference ratio, which is marked as JC. A further technical solution of the present invention is as follows: the method for obtaining the proportion of abnormal sub-curve groups SL is as follows: Based on any sub-curve group; Several data acquisition nodes are set on the temperature sub-curve of the energy storage power supply after it is in operation in the sub-curve group. The temperature values at the acquisition nodes are obtained, and the sum and average values are calculated to obtain the average temperature of the energy storage power supply after it is in operation. Similarly, the average temperature before the energy storage power supply is obtained in the same way as the average temperature after the energy storage power supply is put into operation. The temperature difference is calculated by comparing the average temperature after the energy storage power supply starts operating with the average temperature before the energy storage power supply starts operating, and the absolute value is taken and processed to obtain the temperature difference value. Compare the temperature difference value with the standard temperature difference value; If the temperature difference is greater than the standard temperature difference value, the sub-curve group will be marked as an abnormal sub-curve group. If the temperature difference is less than or equal to the standard temperature difference value, the sub-curve group is marked as a normal sub-curve group. The number of abnormal sub-curve groups is counted, and the ratio of the number of abnormal sub-curve groups to the number of sub-curve groups is calculated. The percentage of abnormal sub-curve groups is then processed and marked as SL. As a further technical solution of the present invention, the method for obtaining the coincidence characterization value is as follows: The sum of the time occupied by the abnormal sub-curve group and the unstable time is calculated, and the ratio is calculated with the overlapping time period to obtain the overlapping time period ratio. The difference between the overlapping time period and the non-overlapping time period is calculated, and the absolute value is taken. The ratio is calculated with the overlapping time period to obtain the overlapping deviation ratio. The overlap period percentage (CH) and overlap deviation percentage (CP) are processed using the formula. The coincidence characterization value BZ is calculated, where a1 and a2 are both preset scaling coefficients; A further technical solution of the present invention is as follows: the method for obtaining the overlap period ratio CH and the overlap deviation ratio CP is as follows: Obtain the time occupied by the abnormal sub-curve group, compare the time occupied by the abnormal sub-curve group with the unstable time, and mark the overlapping time of the time occupied by the abnormal sub-curve group and the unstable time as the overlapping period. The sum of the time occupied by the abnormal sub-curve group and the unstable time is calculated, and the ratio is calculated with the overlapping time period to obtain the proportion of overlapping time period. The difference between the time occupied by the abnormal sub-curve group and the overlapping period is calculated, and the absolute value is taken to obtain the abnormal non-overlapping time. The difference between the unstable time and the overlapping period is calculated, and the absolute value is taken to obtain the unstable non-overlapping time. The non-overlapping time periods are obtained by summing the abnormal non-overlapping time periods and the unstable non-overlapping time periods. The difference between the overlapping and non-overlapping time periods is calculated, and the absolute value is taken. This absolute value is then compared with the overlapping time period to obtain the overlap deviation ratio. A further technical solution of the present invention is as follows: a detection signal acquisition module: acquires dynamic data when the energy storage power supply is working, analyzes the dynamic data to obtain the proportion of unstable time and the proportion of ambient temperature deviation, obtains the temperature change value according to the processing of the proportion of unstable time and the proportion of ambient temperature deviation, compares the temperature change value with the temperature change threshold, and generates a detection signal if the temperature change value is greater than the temperature change threshold. Suspected abnormal signal generation module: Based on the detection signal, acquire the energy storage power supply detection data, acquire the proportion of abnormal sub-curve groups and the relative temperature difference ratio based on the detection data, obtain the curve performance value based on the processing of the proportion of abnormal sub-curve groups and the relative temperature difference ratio, compare the curve performance value with the curve performance threshold, and generate a suspected abnormal signal if the curve performance value is less than the curve performance threshold, prompting the staff to detect the abnormal period of the energy storage power supply. Suspected anomaly signal analysis module: Based on suspected anomaly signals, it acquires overlapping data, analyzes and processes the overlapping data to obtain overlapping characterization values, and compares the overlapping characterization values with the overlapping characterization threshold. If the overlapping characterization value is greater than or equal to the overlapping characterization threshold, it generates an energy storage power prediction signal.
[0007] The beneficial effects of this invention are as follows: 1. The invention acquires dynamic data of the energy storage power supply during operation, analyzes the dynamic data to obtain temperature change values, compares the temperature change values with temperature change thresholds, and generates a detection signal if the temperature change value is greater than the temperature change threshold. This invention significantly improves the safety and stability of energy storage power supply operation by accurately processing the ambient temperature of the energy storage power supply, acquiring temperature change values and generating corresponding detection signals. 2. Based on the detection signal, acquire energy storage power supply detection data, obtain the normal index and temperature relative difference ratio of the sub-curve group based on the detection data, obtain the curve performance value based on the processing of the normal index and temperature relative difference ratio of the sub-curve group, compare the curve performance value with the curve performance threshold, and generate an abnormal signal if the curve performance value is less than the curve performance threshold, prompting the staff to handle it. This invention collects energy storage power supply detection data in real time, uses the normal index and temperature relative difference ratio of the sub-curve group for intelligent analysis, and automatically generates an abnormal signal when the curve performance value is lower than the curve performance threshold, realizing early fault warning and intelligent management, improving system reliability and promoting energy conservation and emission reduction; 3. Based on suspected abnormal signals, overlapping time periods are obtained. Overlapping characteristic values are obtained through analysis and processing of overlapping time periods. Based on the comparison between overlapping characteristic values and overlapping characteristic thresholds, if the overlapping characteristic value is greater than or equal to the overlapping characteristic threshold, an energy storage power prediction signal is generated. This method detects suspected abnormal signals, determines overlapping time periods, calculates overlapping characteristic values and compares them with thresholds. When the overlapping characteristic value reaches or exceeds the threshold, an energy storage power prediction signal is generated. Its beneficial effect is that it can accurately and efficiently identify energy storage power anomalies, and improve the accuracy and efficiency of system monitoring and maintenance. Attached Figure Description
[0008] The invention will now be further described with reference to the accompanying drawings.
[0009] Figure 1 This is a flowchart of a heat exchange anomaly detection system for energy storage power supplies according to the present invention. Figure 2 This is a flowchart of the steps of a fault detection and early warning system for an energy storage power supply according to the present invention. Detailed Implementation
[0010] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.
[0011] Example 1 like Figures 1-2 The fault detection and early warning system for an energy storage power supply according to the embodiment of the present invention shown includes: Detection signal acquisition module: acquires dynamic data when the energy storage power supply is working, analyzes the dynamic data to obtain the proportion of unstable time and the proportion of ambient temperature deviation, and obtains the temperature change value based on the processing of the proportion of unstable time and the proportion of ambient temperature deviation. It should be noted that dynamic data includes ambient temperature values; Multiple temperature sensors are placed in the working area of the energy storage power supply to obtain the ambient temperature value when the energy storage power supply is working. An XY coordinate system is established, where the X-axis represents the working time and the Y-axis represents the ambient temperature value. The temperature of the temperature sensor is marked in the coordinate system according to the time sequence. The marked points are connected to obtain the ambient temperature change curve. It should be explained that the working time refers to the time from the start to the end of the operation of the energy storage power source; In the ambient temperature change curve, the standard value of ambient temperature is used as a reference, and it is marked on the Y-axis to obtain the marked reference point. A straight line parallel to the X-axis is drawn through the reference point and named the reference line. It should be explained that the standard value of ambient temperature is defined by professionals in this field based on a large amount of historical data. The non-steady time is obtained by statistically analyzing the operating time when the ambient temperature change curve of the energy storage power supply exceeds the baseline. The ratio of unstable time to total working time is calculated and processed to obtain the proportion of unstable time. It should be noted that the proportion of unstable time reflects the proportion of time in the ambient temperature change curve when the ambient temperature value exceeds the standard ambient temperature value. The larger the proportion of unstable time, the longer the ambient temperature exceeds the standard ambient temperature value, and the more abnormal the heat generation of the energy storage power supply. Conversely, the smaller the proportion of unstable time, the less the ambient temperature exceeds the standard ambient temperature value, and the better the heat dissipation effect of the energy storage power supply. In the ambient temperature change curve, select the data points that exceed the baseline and connect them to obtain the unsteady temperature change curve; Several sampling points are selected in the unstable temperature change curve. The difference between the ambient temperature value of the sampling point and the standard ambient temperature value is calculated, and the absolute value is taken to obtain the ambient temperature deviation. The ratio of the ambient temperature deviation to the standard ambient temperature value is used to obtain the single temperature deviation ratio. The proportion of ambient temperature deviation is obtained by summing up all individual temperature deviation ratios and taking the average value. The percentage of unstable time is denoted as CZ, and the percentage of ambient temperature deviation is denoted as PC; The proportion of unstable time and the proportion of ambient temperature deviation were processed using the formula. The temperature change value is calculated, where s1 and s2 are preset proportional coefficients; It should be noted that the temperature change value reflects the quantitative standard of heat generation during the operation of the energy storage power supply. It is obtained by processing the product of the proportion of unstable time and the proportion of ambient temperature deviation. The larger the temperature change value, the more serious the abnormal heat generation of the energy storage power supply. Conversely, the smaller the temperature change value, the better the heat dissipation performance of the energy storage power supply.
[0012] Step 2: Compare the temperature change value with the temperature change threshold. If the temperature change value is greater than the temperature change threshold, generate a detection signal. The specific comparison process between the temperature change value and the temperature change threshold is as follows: If the temperature change value is less than or equal to the temperature change threshold, no action is taken; If the temperature change value is greater than the temperature change threshold, a detection signal is generated; The technical solution of this invention is as follows: acquire dynamic data of the energy storage power supply during operation, analyze the temperature change value based on the dynamic data, compare the temperature change value with the temperature change threshold, and generate a detection signal if the temperature change value is greater than the temperature change threshold. This invention obtains the temperature change value and generates the corresponding detection signal by accurately processing the ambient temperature of the energy storage power supply.
[0013] Example 2 Based on Example 1, the fault detection and early warning system for an energy storage power supply described in this embodiment of the invention includes: Step 3: Based on the detection signal, acquire the energy storage power supply detection data, obtain the proportion of abnormal sub-curve groups and the relative temperature difference ratio based on the detection data, and obtain the curve performance value based on the processing of the proportion of abnormal sub-curve groups and the relative temperature difference ratio. It should be explained that the test data includes the temperature of the energy storage power supply before and after it starts operating. Specifically, the temperature before and after the operation of the energy storage power supply is obtained through temperature sensors. An XY coordinate system is established, where the X-axis represents time and the Y-axis represents temperature. The temperature before and after the operation of the energy storage power supply is marked in the coordinate system, and the data points are connected to obtain the temperature curve before and after the operation of the energy storage power supply. The temperature curve before the energy storage power supply is put into operation is divided into several sub-curves of the temperature before the energy storage power supply is put into operation. It needs to be explained that, under normal circumstances, the temperature of the energy storage power supply after it starts operating is higher than the temperature before it starts operating. Therefore, in the temperature curve group, the temperature curve after the energy storage power supply starts operating is above the temperature curve before it starts operating. It should be noted that the curve between two adjacent marker points is a sub-curve; Connect the two ends of the sub-curve with a straight line and measure the slope of the connecting line to obtain the slope of the sub-curve. Similarly, the temperature curve after the energy storage power supply is put into operation and the temperature curve before the energy storage power supply is put into operation in the same way to divide them into several sub-curves of temperature after the energy storage power supply is put into operation. The temperature sub-curves before and after the operation of the energy storage power supply, which are in the same time period, are integrated into a single sub-curve group. Based on any sub-curve group; Several data acquisition nodes are set on the temperature sub-curve of the energy storage power supply after it is in operation in the sub-curve group. The temperature values at the acquisition nodes are obtained, and the sum and average values are calculated to obtain the average temperature of the energy storage power supply after it is in operation. Similarly, the average temperature before the energy storage power supply is put into operation and the average temperature after the energy storage power supply is put into operation in the same way, so as to obtain the average temperature before the energy storage power supply is put into operation. The temperature difference is calculated by comparing the average temperature after the energy storage power supply starts operating with the average temperature before the energy storage power supply starts operating, and the absolute value is taken and processed to obtain the temperature difference value. Compare the temperature difference value with the standard temperature difference value; If the temperature difference is greater than the standard temperature difference value, the sub-curve group will be marked as an abnormal sub-curve group. If the temperature difference is less than or equal to the standard temperature difference value, the sub-curve group is marked as a normal sub-curve group. The number of abnormal sub-curve groups is counted, and the ratio of the number of abnormal sub-curve groups to the number of sub-curve groups is calculated. The percentage of abnormal sub-curve groups is then processed and marked as SL. Based on the abnormal sub-curve group, the temperature difference value corresponding to the abnormal sub-curve group is processed with the temperature difference standard value to obtain the temperature relative difference of the abnormal sub-curve group. The temperature relative differences of all abnormal sub-curve groups are summed and averaged to obtain the temperature relative difference mean. The temperature relative difference mean is processed with the temperature difference standard value to obtain the temperature relative difference ratio, which is marked as JC. The relative temperature difference ratio JC and the proportion of abnormal sub-curve groups SL were processed using the formula. The curve performance value QX is calculated, where a1 and a2 are preset scaling coefficients; It should be noted that the curve performance value QX reflects the quantitative standard of the normal temperature of the energy storage power supply after operation and before operation. The larger the curve performance value QX, the more normal the temperature of the energy storage power supply after operation and before operation. Conversely, the smaller the curve performance value QX, the more abnormal the temperature of the energy storage power supply after operation and before operation. By comparing and processing the curve performance values, it is possible to help observe the normal condition of the energy storage power supply. Step 4: Compare the curve performance value with the curve performance threshold. If the curve performance value is less than the curve performance threshold, a suspected abnormal signal is generated to prompt the staff to detect the abnormal period of the energy storage power supply. The specific comparison process between the curve performance value and the curve performance threshold is as follows: If the curve value is less than the curve threshold, a suspected abnormal signal is generated. If the curve performance value is greater than or equal to the curve performance threshold, it indicates that the energy storage power supply has not met the abnormal standard. The technical solution of this invention is as follows: Based on the detection signal, energy storage power supply detection data is acquired; based on the detection data, the proportion of abnormal sub-curve groups and the relative temperature difference ratio are obtained; based on the processing of the proportion of abnormal sub-curve groups and the relative temperature difference ratio, a curve performance value is obtained; the curve performance value is compared with a curve performance threshold; if the curve performance value is less than the curve performance threshold, a suspected abnormal signal is generated, prompting staff to detect the abnormal period of the energy storage power supply. This invention collects energy storage power supply detection data in real time, uses the proportion of abnormal sub-curve groups and the relative temperature difference ratio for intelligent analysis, and automatically generates a suspected abnormal signal when the curve performance value is lower than the curve performance threshold, thereby realizing early fault warning and intelligent management, improving system reliability and promoting operational safety.
[0014] Example 3 Based on Embodiments 1 and 2, the fault detection and early warning system for an energy storage power supply described in this embodiment of the invention includes: Step 5: Based on the suspected abnormal signal, obtain overlapping data, analyze and process the overlapping data to obtain the overlapping characterization value, and compare the overlapping characterization value with the overlapping characterization threshold. If the overlapping characterization value is greater than or equal to the overlapping characterization threshold, then generate the energy storage power prediction signal. It should be explained that overlapping data includes overlapping time periods; Obtain the time occupied by the abnormal sub-curve group, compare the time occupied by the abnormal sub-curve group with the unstable time, and mark the overlapping time of the time occupied by the abnormal sub-curve group and the unstable time as the overlapping period. The sum of the time occupied by the abnormal sub-curve group and the unstable time is calculated, and the ratio is calculated with the overlapping time period to obtain the proportion of overlapping time period. The difference between the time occupied by the abnormal sub-curve group and the overlapping period is calculated, and the absolute value is taken to obtain the abnormal non-overlapping time. The difference between the unstable time and the overlapping period is calculated, and the absolute value is taken to obtain the unstable non-overlapping time. The non-overlapping time periods are obtained by summing the abnormal non-overlapping time periods and the unstable non-overlapping time periods. The difference between the overlapping and non-overlapping time periods is calculated, and the absolute value is taken. This absolute value is then compared with the overlapping time period to obtain the overlap deviation ratio. The percentage of overlapping periods is marked as CH, and the percentage of overlapping deviations is marked as CP; The overlap period percentage (CH) and overlap deviation percentage (CP) are processed using the formula. The coincidence characterization value BZ is calculated, where a1 and a2 are both preset scaling coefficients; The overlap characterization value is compared with the overlap characterization threshold. The specific comparison process is as follows: If the overlap characterization value is greater than or equal to the overlap characterization threshold, then an energy storage power prediction signal is generated. If the overlap representation value is less than the overlap representation threshold, no action is taken. The technical solution of this invention is as follows: based on suspected abnormal signals, overlapping time periods are obtained; based on the analysis and processing of overlapping time periods, overlapping characterization values are obtained; based on the comparison between overlapping characterization values and overlapping characterization thresholds, if the overlapping characterization values are greater than or equal to the overlapping characterization thresholds, an energy storage power prediction signal is generated. This method determines overlapping time periods by detecting suspected abnormal signals, calculates overlapping characterization values and compares them with thresholds, and generates an energy storage power prediction signal when the overlapping characterization values reach or exceed the thresholds. Its beneficial effect is that it can accurately and efficiently identify energy storage power anomalies, and improve the accuracy and efficiency of system monitoring and maintenance.
[0015] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. A fault detection and early warning system for an energy storage power source, characterized in that: include: Detection signal acquisition module: acquires dynamic data when the energy storage power supply is working, analyzes the dynamic data to obtain the proportion of unstable time and the proportion of ambient temperature deviation, and obtains the temperature change value based on the processing of the proportion of unstable time and the proportion of ambient temperature deviation. The temperature change value is compared with the temperature change threshold. If the temperature change value is greater than the temperature change threshold, a detection signal is generated. Suspected abnormal signal generation module: Based on the detection signal, acquire the energy storage power supply detection data, acquire the proportion of abnormal sub-curve groups and the relative temperature difference ratio based on the detection data, obtain the curve performance value based on the processing of the proportion of abnormal sub-curve groups and the relative temperature difference ratio, compare the curve performance value with the curve performance threshold, and generate a suspected abnormal signal if the curve performance value is less than the curve performance threshold, prompting the staff to detect the abnormal period of the energy storage power supply. Suspected anomaly signal analysis module: Based on suspected anomaly signals, it acquires overlapping data, analyzes and processes the overlapping data to obtain overlapping characterization values, and compares the overlapping characterization values with the overlapping characterization threshold. If the overlapping characterization value is greater than or equal to the overlapping characterization threshold, it generates an energy storage power prediction signal.
2. The fault detection and early warning system for an energy storage power supply according to claim 1, characterized in that: The percentage of unstable time is denoted as CZ, and the percentage of ambient temperature deviation is denoted as PC; Through formula The temperature change value is calculated, where s1 and s2 are preset proportional coefficients.
3. The fault detection and early warning system for an energy storage power supply according to claim 2, characterized in that: The method for obtaining the percentage of unstable time is as follows: Obtain the ambient temperature value around the energy storage power supply, establish an XY coordinate system, where the X-axis represents the working time and the Y-axis represents the ambient temperature value, mark the temperature sensor temperature in the coordinate system according to the time sequence, and connect the marked points to obtain the ambient temperature change curve. In the ambient temperature change curve, a baseline is drawn based on the standard ambient temperature value; The non-steady time is obtained by statistically analyzing the operating time when the ambient temperature change curve of the energy storage power supply exceeds the baseline. The ratio of unstable time to total working time is calculated to obtain the percentage of unstable time.
4. The fault detection and early warning system for an energy storage power supply according to claim 2, characterized in that: The method for obtaining the percentage of ambient temperature deviation is as follows: In the ambient temperature change curve, select the data points that exceed the baseline and connect them to obtain the unsteady temperature change curve; Several sampling points are selected in the unstable temperature change curve. The difference between the ambient temperature value of the sampling point and the standard ambient temperature value is calculated, and the absolute value is taken to obtain the ambient temperature deviation. The ratio of the ambient temperature deviation to the standard ambient temperature value is used to obtain the single temperature deviation ratio. The percentage of ambient temperature deviation is obtained by summing all individual temperature deviation ratios and taking the average.
5. The fault detection and early warning system for an energy storage power supply according to claim 1, characterized in that: The relative temperature difference ratio JC and the proportion of abnormal sub-curve groups SL were processed using the formula. The curve performance value QX is calculated, where a1 and a2 are preset scaling coefficients.
6. The fault detection and early warning system for an energy storage power supply according to claim 5, characterized in that: The method for obtaining the relative temperature difference ratio JC is as follows: Based on the abnormal sub-curve group, the temperature difference value corresponding to the abnormal sub-curve group is processed with the temperature difference standard value to obtain the temperature relative difference of the abnormal sub-curve group. The temperature relative differences of all abnormal sub-curve groups are summed and averaged to obtain the temperature relative difference mean. The temperature relative difference mean is compared with the temperature difference standard value to obtain the temperature relative difference ratio, which is marked as JC.
7. The fault detection and early warning system for an energy storage power supply according to claim 5, characterized in that: The method for obtaining the percentage of abnormal sub-curve groups (SL) is as follows: Based on any sub-curve group; Several data acquisition nodes are set on the temperature sub-curve of the energy storage power supply after it is in operation in the sub-curve group. The temperature values at the acquisition nodes are obtained, and the sum and average values are calculated to obtain the average temperature of the energy storage power supply after it is in operation. Similarly, the average temperature before the energy storage power supply is obtained in the same way as the average temperature after the energy storage power supply is put into operation. The temperature difference is calculated by comparing the average temperature after the energy storage power supply starts operating with the average temperature before the energy storage power supply starts operating, and the absolute value is taken and processed to obtain the temperature difference value. Compare the temperature difference value with the standard temperature difference value; If the temperature difference is greater than the standard temperature difference value, the sub-curve group will be marked as an abnormal sub-curve group. The number of abnormal sub-curve groups is counted, and the ratio of the number of abnormal sub-curve groups to the number of sub-curve groups is calculated. The percentage of abnormal sub-curve groups is then processed and labeled as SL.
8. The fault detection and early warning system for an energy storage power supply according to claim 1, characterized in that: The method for obtaining the overlap characterization value is as follows: The sum of the time occupied by the abnormal sub-curve group and the unstable time is calculated, and the ratio is calculated with the overlapping time period to obtain the overlapping time period ratio. The difference between the overlapping time period and the non-overlapping time period is calculated, and the absolute value is taken. The ratio is calculated with the overlapping time period to obtain the overlapping deviation ratio. The overlap period percentage (CH) and overlap deviation percentage (CP) are processed using the formula. The coincidence characterization value BZ is calculated, where a1 and a2 are preset scaling coefficients.
9. The fault detection and early warning system for an energy storage power supply according to claim 8, characterized in that: The method for obtaining the overlap period percentage (CH) is as follows: Obtain the time occupied by the abnormal sub-curve group, compare the time occupied by the abnormal sub-curve group with the unstable time, and mark the overlapping time of the time occupied by the abnormal sub-curve group and the unstable time as the overlapping period. The time occupied by the abnormal sub-curve group is summed with the unstable time, and the ratio of this sum to the overlapping time period is calculated to obtain the overlapping time period percentage CH.
10. A fault detection and early warning system for an energy storage power supply according to claim 8, characterized in that: The overlap deviation percentage (CP) is obtained as follows: The difference between the time occupied by the abnormal sub-curve group and the overlapping period is calculated, and the absolute value is taken to obtain the abnormal non-overlapping time. The difference between the unstable time and the overlapping period is calculated, and the absolute value is taken to obtain the unstable non-overlapping time. The non-overlapping time periods are obtained by summing the abnormal non-overlapping time periods and the unstable non-overlapping time periods. The difference between the overlapping and non-overlapping time periods is calculated, and the absolute value is taken. This absolute value is then compared with the overlapping time period to obtain the overlap deviation ratio (CP).