A substation battery fault monitoring and alarming system

Abstract

This invention relates to the field of battery monitoring technology, and more particularly to a substation battery fault monitoring and alarm system. The invention uses a data acquisition module to detect dust concentration at the terminals of each battery cell and collect data on the charge capacity under the float charge voltage of each battery cell. A feature capture module constructs dust concentration change curves at the terminals of each battery cell and delineates the time periods of significant influence. A correlation identification module filters related battery cell groups. A float charge capacity analysis module determines the charge growth range corresponding to each charge change curve in the related battery cell groups. A risk warning module extracts reference durations corresponding to the charge growth ranges of unfiltered battery cells from historical data, and determines whether the battery is at risk of undervoltage operation due to disturbance based on the comparison between the reference duration and the actual duration. Furthermore, by improving the representativeness of the monitoring data, the invention enables early monitoring and alarm of substation battery faults.

Description

Technical Field

[0001] This invention relates to the field of battery monitoring technology, and in particular to a substation battery fault monitoring and alarm system. Background Technology

[0002] As the core backup power source of the power system, substation batteries play a crucial role in providing continuous power to critical loads such as protection devices and communication systems during emergency scenarios such as power grid failures and fault switching. With the increasing age of substations and the influence of environmental factors, dust contaminants easily accumulate at the terminals of each battery cell during long-term float charging operation. This dust mainly originates from particulate matter carried by the substation's indoor ventilation airflow, metal debris generated during equipment operation, and dust settling from the environment. Its accumulation at the terminals can not only increase contact resistance but also cause sudden concentration changes under conditions such as airflow disturbances, thereby affecting the charging and discharging characteristics of the battery cells.

[0003] For example, Chinese Patent Publication No. CN116125317A discloses a battery monitoring and analysis method, device, and system, relating to the field of power supply monitoring. The method includes acquiring voltage monitoring data of the battery pack and its individual cells; based on the voltage monitoring data, determining whether conditions one and two are simultaneously met. Condition one is: the difference between the voltage monitoring values ​​of at least two individual cells and the nominal float charge voltage is greater than a preset voltage difference; condition two is: the rate of decrease in the voltage monitoring values ​​of the remaining individual cells exceeding a preset proportion relative to the voltage acquisition value at the data recording preparation time is within a certain range, where the data recording preparation time is the moment when the voltage monitoring value of the battery pack is less than the preset voltage; if conditions one and two are simultaneously met, it is determined that a single-cell replacement event has occurred in the battery pack. Based on the monitoring voltage value change pattern, the existence of a single-cell replacement event in the battery pack is automatically and accurately identified.

[0004] Existing technologies do not consider the hidden impact of dust ionization on battery operation, cannot accurately locate battery cell clusters affected by dust, and are difficult to identify undervoltage operation risks caused by dust in advance, resulting in delayed fault warnings. Summary of the Invention

[0005] To address this, the present invention provides a substation battery fault monitoring and alarm system to overcome the problems of existing technologies being unable to accurately locate battery cell clusters affected by dust interference, making it difficult to identify undervoltage operation risks caused by dust in advance, resulting in delayed fault warnings.

[0006] To achieve the above objectives, the present invention provides a substation battery fault monitoring and alarm system, comprising: The data acquisition module includes a dust monitoring unit for detecting the dust concentration at the terminal of each battery cell and a power monitoring unit for collecting power data of each battery cell under the float charge voltage. The feature capture module, which is connected to the data acquisition module, is used to construct the dust concentration change curve of each battery cell terminal, and to determine the obvious feature points and delineate the obvious influence time period on the concentration change curve. The correlation identification module, which is connected to the feature capture module, is used to determine whether the time periods of the dominant influence on each concentration change curve are correlated, and to filter out battery cells whose time periods of dominant influence are temporally correlated. A float charge analysis module is connected to the data acquisition module and the association identification module respectively, and is used to obtain the charge change curve of the selected battery cells during the fault analysis period, and determine the charge growth range corresponding to each charge change curve. The risk warning module, which is connected to the float charge analysis module, is used to extract the reference duration of the float charge of unscreened battery cells in the charge growth range from historical data, and to determine whether the battery is at risk of being disturbed and operating under voltage based on the comparison between the reference duration and the actual duration.

[0007] Furthermore, the feature capture module is used to receive dust concentration data collected in real time at the terminal locations of each battery cell, and generate a dust concentration change curve corresponding to each battery cell terminal with time as the horizontal axis and dust concentration as the vertical axis.

[0008] Furthermore, the feature capture module is used to determine dominant feature points and delineate the dominant influence time period, wherein, The feature capture module calculates the slope of each sampling point on the dust concentration change curve and determines the sampling point with the largest slope as the dominant feature point. The feature capture module uses the time corresponding to the dominant feature point as the central reference and extends forward and backward by a preset time to define the dominant influence time period.

[0009] Furthermore, the correlation identification module is used to determine the correlation of the time periods of explicit influence, wherein, The association identification module extracts the start and end times of the explicit influence time period corresponding to each battery unit, and calculates the time overlap ratio of any two battery units' explicit influence time periods. If the time overlap ratio is not 0, the correlation identification module determines that the time periods of explicit influence between the two battery cells are correlated.

[0010] Furthermore, the association identification module is used to filter battery cells with time correlation, wherein, The association identification module integrates all battery cells that are associated with the time period of explicit influence of at least one other battery cell into an associated battery cell group.

[0011] Furthermore, the float charge analysis module is used to determine the fault analysis period, wherein, The float charge analysis module extracts the earliest start time and latest end time of all explicit influence time periods corresponding to the battery cells in the correlated battery cell group, and defines the time interval from the earliest start time to the latest end time as the fault analysis period.

[0012] Furthermore, the floating charge power analysis module is used to generate a power change curve and determine the power growth range, wherein, The float charge power analysis module receives power data of battery cells in the associated battery cell group collected by the power monitoring unit during the fault analysis period, generates power change curves for each battery cell, and determines the power corresponding to the start time and end time of the fault analysis period on the power change curves to determine the power growth range of each battery cell.

[0013] Furthermore, the risk warning module is used to extract a reference duration, wherein, The risk warning module is used to match the time required for non-associated battery cells to charge to the same amount of power as the power growth range under the same float charge voltage conditions from the historical database, and extract the time as the reference time.

[0014] Furthermore, the risk warning module is used to determine the actual duration, wherein, The risk warning module determines the time span of the battery cells within the associated battery cell group within the corresponding power growth interval, and defines the time span as the actual duration.

[0015] Furthermore, the risk warning module is used to determine whether the battery is at risk of undervoltage operation due to disturbance. The risk warning module is used to calculate the absolute value of the difference between the reference duration and the actual duration corresponding to the charge growth range of the battery cells in the associated battery cell group; If the absolute value of the difference is greater than a preset difference threshold, the risk warning module determines that the battery is at risk of being disturbed and operating under voltage.

[0016] The beneficial effects of the technical solution presented in this application include: detecting dust concentration at the terminals of each battery cell and collecting charge data under float charge voltage of each battery cell through a data acquisition module; constructing dust concentration change curves at the terminals of each battery cell and defining the time period of significant influence through a feature capture module; screening related battery cell groups through an association identification module; determining the charge growth range corresponding to each charge change curve in the related battery cell group through a float charge charge analysis module; and determining whether the battery is at risk of undervoltage operation due to disturbance through a risk warning module. Furthermore, by improving the representativeness of the monitoring data, the risk of undervoltage operation caused by dust can be identified in advance, enabling timely fault monitoring and alarm for substation batteries.

[0017] Furthermore, in this invention, the dust concentration change at the battery unit terminal of the substation is usually related to environmental airflow disturbance and dust ionization and migration. The sharp change in dust concentration is a key signal that dust has a significant impact on the conductivity of the battery terminal. The sampling point with the largest slope means that the change in dust concentration is more obvious at that moment, thus accurately locking the key time point at which dust may interfere with battery operation.

[0018] Furthermore, by extracting the earliest start time and the latest end time of all overtly influential time periods within the group, this invention defines the time interval as the fault analysis period, which can fully cover the entire cycle of the impact of the same systematic dust disturbance on all battery cells in the cluster, providing complete time-dimensional data support.

[0019] Furthermore, in this invention, each battery cell within the correlated battery cell group is affected by the same systematic dust disturbance, and the fault analysis period has completely covered the entire cycle of the dust disturbance. Collecting power data during this period can accurately capture the true change pattern of battery float charge under dust interference, avoiding the inclusion of normal power data during non-disturbance periods that could lead to analysis bias.

[0020] Furthermore, by establishing a comparison between the reference duration under normal operating conditions and the actual duration under disturbed operating conditions, the present invention can accurately quantify the degree of interference of dust disturbance on the growth of battery power. Attached Figure Description

[0021] Figure 1 This is a system block diagram of a substation battery fault monitoring and alarm system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the logic for determining the correlation of explicit influence time periods in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the logic of determining whether a battery is at risk of undervoltage operation due to disturbance, according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0025] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Please see Figure 1 The diagram shown is a system block diagram of a substation battery fault monitoring and alarm system. The substation battery fault monitoring and alarm system of this invention includes: The data acquisition module includes a dust monitoring unit for detecting the dust concentration at the terminal of each battery cell and a power monitoring unit for collecting power data of each battery cell under the float charge voltage. This invention does not specifically limit the dust monitoring unit; it can be installed 5cm away from the battery unit terminals and uses laser scattering to collect dust concentration data at a sampling frequency of 1 time per second. The unit of dust concentration is μg / m³. 3 .

[0027] This invention does not specifically limit the power monitoring unit. Real-time monitoring of battery power by the power management SOC is an existing technology and will not be elaborated here.

[0028] The feature capture module, which is connected to the data acquisition module, is used to construct the dust concentration change curve of each battery cell terminal, and to determine the obvious feature points and delineate the obvious influence time period on the concentration change curve. The correlation identification module, which is connected to the feature capture module, is used to determine whether the time periods of the dominant influence on each concentration change curve are correlated, and to filter out battery cells whose time periods of dominant influence are temporally correlated. A float charge analysis module is connected to the data acquisition module and the association identification module respectively, and is used to obtain the charge change curve of the selected battery cells during the fault analysis period, and determine the charge growth range corresponding to each charge change curve. The risk warning module, which is connected to the float charge analysis module, is used to extract the reference duration of the float charge of unscreened battery cells in the charge growth range from historical data, and to determine whether the battery is at risk of being disturbed and operating under voltage based on the comparison between the reference duration and the actual duration.

[0029] In this invention, the risk warning module is also connected to a buzzer. If it is determined that the battery is at risk of being disturbed and operating under voltage, the buzzer will be controlled to issue an alarm.

[0030] This invention does not limit the specific structure of the feature capture module, the association identification module, the floating charge analysis module, and the risk warning module. They can be logic components, such as field-programmable logic components, microprocessors, processors used in computers, etc., which will not be elaborated here.

[0031] In this invention, the float charge voltage is a technical parameter for the daily operation of a substation battery bank. It is defined as the constant voltage that the charging device continuously outputs to maintain the battery at full charge during long-term standby. This is crucial for ensuring that the battery capacity does not decay and that it can quickly respond to discharge demands. Under stable float charge voltage conditions, the battery's charge increase should be gradual and continuous. The rate of increase is related to the battery's internal resistance and the contact resistance at the connection points. When the contact resistance at the battery cell connection points increases due to dust accumulation, oxidation, corrosion, or other factors, the equivalent resistance of the battery's charging circuit rises, leading to a decrease in charging current and a slower rate of charge increase. This manifests as a significant increase in the time required to charge the same amount of battery capacity.

[0032] In this invention, the modules communicate with each other via a CAN bus.

[0033] Specifically, the feature capture module is used to receive dust concentration data collected in real time at the terminal locations of each battery cell, and generate a dust concentration change curve corresponding to each battery cell terminal with time as the horizontal axis and dust concentration as the vertical axis.

[0034] In this invention, the unit of time on the horizontal axis is seconds (s), and the unit of dust concentration on the vertical axis is μg / m³. 3 .

[0035] Specifically, the feature capture module is used to determine dominant feature points and delineate the dominant influence time period, wherein, The feature capture module calculates the slope of each sampling point on the dust concentration change curve and determines the sampling point with the largest slope as the dominant feature point. The feature capture module uses the time corresponding to the dominant feature point as the central reference and extends forward and backward by a preset time to define the dominant influence time period.

[0036] This invention does not limit the method of calculating the slope of each sampling point on the dust concentration change curve. It can use the coordinates of two adjacent points to calculate the slope at the sampling point. The smaller the distance between two adjacent points, the more accurate the calculated slope of the sampling point. This is a common method for calculating the slope of any point on the curve, and will not be elaborated here.

[0037] In the implementation of this invention, multiple experiments were conducted on the dust accumulation and dissipation process at the battery terminals of the same type of substation. The average time for the dust concentration to change from change to stability was recorded as 12s. With a redundancy allowance, the preset duration was optionally determined to be 15s, so the duration of each obvious influence period was 30s.

[0038] Understandably, changes in dust concentration at the battery terminal terminals of substations are typically related to environmental airflow disturbances and dust ionization and migration. A sharp change in dust concentration is a key signal that dust significantly impacts the conductivity of the battery terminals. The sampling point with the steepest slope indicates a significant change in dust concentration at that moment, thus accurately pinpointing the critical time point where dust may interfere with battery operation. Extending this significant characteristic point forward and backward by a preset time period defines the significant impact timeframe because sudden changes in dust concentration caused by airflow disturbances require a certain amount of time to achieve stable accumulation or dissipation at the terminals. During this period, the dust's presence continuously affects the terminal contact resistance, thereby interfering with the battery's charge growth characteristics under float charging conditions, thus improving the targeting and accuracy of fault monitoring.

[0039] Specifically, please refer to Figure 2 The diagram shown is a logical flowchart for determining the correlation of time periods with obvious influence according to an embodiment of the present invention. The correlation identification module is used to determine the correlation of time periods with obvious influence. The association identification module extracts the start and end times of the explicit influence time period corresponding to each battery unit, and calculates the time overlap ratio of any two battery units' explicit influence time periods. If the time overlap ratio is 0, the correlation identification module determines that the time periods of explicit influence between the two battery cells are not correlated. If the time overlap ratio is not 0, the correlation identification module determines that the time periods of explicit influence between the two battery cells are correlated.

[0040] For example, the time overlap ratio in the implementation of the present invention is (overlap duration / shorter duration of the dominant influence time of the two battery cells) × 100%.

[0041] It is understandable that the fluctuations in dust concentration in substations are mostly caused by external factors and are not isolated pollution from a single battery cell. If the dust concentration at the terminals of multiple battery cells shows obvious changes and these time periods overlap, it indicates that these battery cells are affected by the same external disturbance, and their dust ionization is a related phenomenon with the same origin.

[0042] Specifically, the association identification module is used to filter battery cells with time correlation, wherein, The association identification module integrates all battery cells that are associated with the time period of explicit influence of at least one other battery cell into an associated battery cell group.

[0043] For example, the association identification module can assign a unique identifier to each battery cell (e.g., number U1, U2, ..., Un) and initialize it to an "ungrouped" state; By calculating the time overlap ratio of the dominant influence time periods of any two battery cells, all battery cells are traversed. If battery cell U1 is associated with battery cell U2, and battery cell U2 is associated with battery cell U3, then battery cell U1 and battery cell U3 are determined to be associated, and battery cells U1, U2, and U3 are grouped into the same temporary cluster. This process is repeated to aggregate all associated battery cells into one or more temporary clusters. Each temporary cluster is a group of associated battery cells, and all battery cells in the group have a time correlation with at least one other battery cell in the group that has a dominant influence time period. Battery cells that do not have a time correlation with any other battery cell are determined to be unassociated battery cells.

[0044] Specifically, the float charge analysis module is used to determine the fault analysis period, wherein, The float charge analysis module extracts the earliest start time and latest end time of all explicit influence time periods corresponding to the battery cells in the correlated battery cell group, and defines the time interval from the earliest start time to the latest end time as the fault analysis period.

[0045] Understandably, while the start and end times of the apparent impact periods for each battery cell within a cluster may differ due to factors such as dust propagation in space, they are essentially time segments under the influence of the same systemic dust disturbance. Using only the apparent impact period of a single battery cell as the scope of analysis would miss the periods when other related cells are affected by dust, leading to incomplete power data collection. By extracting the earliest start and latest end times of all apparent impact periods within the cluster and defining this time interval as the fault analysis period, we can comprehensively cover the entire cycle of the impact of the same systemic dust disturbance on all battery cells within the cluster, providing complete temporal data support.

[0046] Specifically, the float charge power analysis module is used to generate a power change curve and determine the power growth range, wherein, The float charge power analysis module receives power data of battery cells in the associated battery cell group collected by the power monitoring unit during the fault analysis period, generates power change curves for each battery cell, and determines the power corresponding to the start time and end time of the fault analysis period on the power change curves to determine the power growth range of each battery cell.

[0047] In this invention, the horizontal axis of the power change curve represents time, using a 24-hour time system, and the vertical axis represents power data, with the unit being %. Power data = (current power in the battery cell / rated battery capacity) × 100%.

[0048] For example, the fault analysis period for a certain associated battery cell group is determined to be T_start=08:00 and T_end=08:06. The charge change curves of each battery cell in the associated battery cell group are matched with the vertical charge data Q_start corresponding to T_start=08:00 and the vertical charge data Q_end corresponding to T_end=08:06 by matching the horizontal axis time coordinate. Then the charge growth range of the battery cell is [Q_start, Q_end]. From the perspective of electrochemical mechanism, the target of dust interference is the contact resistance of the battery connection terminal, which only affects the equivalent resistance of the charging circuit, resulting in a decrease in charging current. This is manifested as a smaller charge growth in the same time period. Therefore, in the implementation of this invention, the charge of the battery cell during the fault analysis period is Q_end>Q_start.

[0049] Understandably, this invention, by generating charge change curves for each battery cell, can intuitively present the dynamic trend of charge change over time during dust disturbance. The charge at the start of the fault analysis period serves as the battery charge baseline under the initial dust disturbance state, while the charge at the end of the fault analysis period represents the battery charge state at the end of the dust disturbance. The charge growth range defined by these two values ​​can comprehensively characterize the cumulative charge effect of the battery during the continuous dust disturbance. This approach not only eliminates interference from irrelevant charge data outside the fault analysis period but also locks in the direct correlation between dust disturbance and charge growth through a clear time boundary. This ensures that the charge growth range accurately reflects the changes in battery float charging performance under the influence of dust, improving the pertinence and accuracy of charge characteristic analysis and risk assessment.

[0050] Specifically, the risk warning module is used to extract a reference duration, wherein, The risk warning module is used to match the time required for non-associated battery cells to charge to the same amount of power as the power growth range under the same float charge voltage conditions from the historical database, and extract the time as the reference time.

[0051] For example, based on the determined capacity growth range [Q_start, Q_end] of the associated battery cells, the charging time of the non-associated battery cells in the capacity growth range [Q_start, Q_end] is determined as a reference time, and the unit of the reference time is min.

[0052] The historical database in this invention is a database used to store the operating data of the battery cells of the storage battery under the float charge voltage, and the data is aggregated through a data gateway.

[0053] Specifically, the risk warning module is used to determine the actual duration, wherein, The risk warning module determines the time span of the battery cells within the associated battery cell group within the corresponding power growth interval, and defines the time span as the actual duration.

[0054] In this invention, if the fault analysis period of a certain associated battery cell group is determined to be T_start=08:00 and T_end=08:06, then the actual duration is 6 minutes.

[0055] Understandably, batteries within uncorrelated battery cells are not affected by the same systemic dust disturbance. Their charge growth under the same float charge voltage is unaffected by changes in terminal contact resistance caused by dust, and they are in a normal float charge state. Extracting the time required for these batteries to charge to the same level as the disturbed battery from historical databases serves as a reference time, allowing the construction of a normal charge growth time benchmark under dust-free conditions, ensuring the objectivity and comparability of the reference data. However, batteries within correlated battery cell groups are in the fault analysis period affected by dust disturbance. Their charge growth range is the cumulative result of continuous dust disturbance, and the corresponding time span directly reflects the impact of dust disturbance on battery float charge efficiency.

[0056] In this invention, if dust increases the contact resistance at the terminals, it will slow down the rate of charge increase, causing the actual duration to exceed the reference duration. By establishing a comparison between the reference duration under normal operating conditions and the actual duration under disturbed operating conditions, the degree of interference of dust disturbance on battery charge increase can be accurately quantified.

[0057] Specifically, please refer to Figure 3 The diagram shown is a flowchart illustrating the logic of determining whether a battery is at risk of undervoltage operation due to disturbance, according to an embodiment of the present invention. The risk warning module is used to determine whether a battery is at risk of undervoltage operation due to disturbance. The risk warning module is used to calculate the absolute value of the difference between the reference duration and the actual duration corresponding to the charge growth range of the battery cells in the associated battery cell group; If the absolute value of the difference is not greater than the preset difference threshold, the risk warning module determines that the battery does not have the risk of being disturbed and operating under voltage. If the absolute value of the difference is greater than the preset difference threshold, the risk warning module determines that the battery is at risk of being disturbed and operating under voltage.

[0058] In this invention, the preset difference threshold is the critical value at which dust interference has a substantial impact on battery charge growth. Its specific value is determined based on prior testing. A sample group is formed by pre-selecting battery cells of the same model and operating age as the battery to be monitored in the substation. Charge growth data of the sample group is continuously collected under the same float charge voltage. The duration corresponding to different charge increment intervals is recorded. The difference threshold between the reference duration and the actual duration for each charge increment interval is determined by calculating the average duration difference of the battery cells in the sample group within the same charge increment interval. Optionally, for a 100Ah lead-acid battery, when the charge increment is ≤5%, the preset difference threshold is 20s; when 5% < charge increment ≤15%, the preset difference threshold is 40s; and when the charge increment is >15%, the preset difference threshold is 60s.

[0059] It is understandable that the batteries in the associated battery cell group may be affected by dust disturbance, which may increase the contact resistance of their terminals. This will cause the rate of charge growth in the float charging state to slow down abnormally. As a result, the actual time required to charge the same amount of power is significantly longer than the reference time under normal operating conditions. This decrease in charge growth efficiency will gradually lead to undervoltage operation of the battery. In the long run, this will lead to battery capacity decay and reduced power supply reliability.

[0060] This invention directly quantifies the degree of interference of dust disturbance on the battery's charge growth by calculating the absolute value of the difference between the reference duration and the actual duration corresponding to the charge growth interval of the battery cells within the correlated battery cell group. The larger the absolute value of the difference, the more obvious the decrease in float charging efficiency caused by dust and the deeper the degree of battery disturbance.

[0061] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A substation battery failure monitoring and alerting system, characterized by, include: The data acquisition module includes a dust monitoring unit for detecting the dust concentration at the terminal of each battery cell and a power monitoring unit for collecting power data of each battery cell under the float charge voltage. The feature capture module, which is connected to the data acquisition module, is used to construct the dust concentration change curve of each battery cell terminal, and to determine the obvious feature points and delineate the obvious influence time period on the concentration change curve. The correlation identification module, which is connected to the feature capture module, is used to determine whether the time periods of the dominant influence on each concentration change curve are correlated, and to filter out battery cells whose time periods of dominant influence are temporally correlated. A float charge analysis module is connected to the data acquisition module and the association identification module respectively, and is used to obtain the charge change curve of the selected battery cells during the fault analysis period, and determine the charge growth range corresponding to each charge change curve. The risk warning module, which is connected to the float charge analysis module, is used to extract the reference duration of the float charge of unscreened battery cells in the charge growth range from historical data, and to determine whether the battery is at risk of being disturbed and operating under voltage based on the comparison between the reference duration and the actual duration.

2. The substation battery failure monitoring alarm system of claim 1, wherein, The feature capture module is used to receive dust concentration data collected in real time at the terminal locations of each battery cell, and generate a dust concentration change curve corresponding to each battery cell terminal with time as the horizontal axis and dust concentration as the vertical axis.

3. The substation battery fault monitoring and alarm system according to claim 1, characterized in that, The feature capture module is used to determine dominant feature points and delineate the dominant influence time period, wherein, The feature capture module calculates the slope of each sampling point on the dust concentration change curve and determines the sampling point with the largest slope as the dominant feature point. The feature capture module uses the time corresponding to the dominant feature point as the central reference and extends forward and backward by a preset time to define the dominant influence time period.

4. The substation battery fault monitoring and alarm system according to claim 3, characterized in that, The correlation identification module is used to determine the correlation of the time period of explicit influence, wherein, The association identification module extracts the start and end times of the explicit influence time period corresponding to each battery unit, and calculates the time overlap ratio of any two battery units' explicit influence time periods. If the time overlap ratio is not 0, the correlation identification module determines that the time periods of explicit influence between the two battery cells are correlated.

5. The substation battery fault monitoring and alarm system according to claim 4, characterized in that, The correlation identification module is used to filter battery cells with time correlation, wherein... The association identification module integrates all battery cells that are associated with the time period of explicit influence of at least one other battery cell into an associated battery cell group.

6. The substation battery fault monitoring and alarm system according to claim 5, characterized in that, The float charge analysis module is used to determine the fault analysis period, wherein, The float charge analysis module extracts the earliest start time and latest end time of all explicit influence time periods corresponding to the battery cells in the correlated battery cell group, and defines the time interval from the earliest start time to the latest end time as the fault analysis period.

7. The substation battery fault monitoring and alarm system according to claim 6, characterized in that, The floating charge power analysis module is used to generate a power change curve and determine the power growth range, wherein... The float charge power analysis module receives power data of battery cells in the associated battery cell group collected by the power monitoring unit during the fault analysis period, generates power change curves for each battery cell, and determines the power corresponding to the start time and end time of the fault analysis period on the power change curves to determine the power growth range of each battery cell.

8. The substation battery fault monitoring and alarm system according to claim 7, characterized in that, The risk warning module is used to extract the reference duration, wherein, The risk warning module is used to match the time required for non-associated battery cells to charge to the same amount of power as the power growth range under the same float charge voltage conditions from the historical database, and extract the time as the reference time.

9. The substation battery fault monitoring and alarm system according to claim 8, characterized in that, The risk warning module is used to determine the actual duration, wherein, The risk warning module determines the time span of the battery cells within the associated battery cell group within the corresponding power growth interval, and defines the time span as the actual duration.

10. The substation battery fault monitoring and alarm system according to claim 9, characterized in that, The risk warning module is used to determine whether the battery is at risk of undervoltage operation due to disturbance. The risk warning module is used to calculate the absolute value of the difference between the reference duration and the actual duration corresponding to the charge growth range of the battery cells in the associated battery cell group; If the absolute value of the difference is greater than a preset difference threshold, the risk warning module determines that the battery is at risk of being disturbed and operating under voltage.

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