Active safety monitoring method for energy storage power station

Abstract

The invention discloses an active safety monitoring method for an energy storage power station, which relates to the field of safety detection of the energy storage power station, and comprises the following steps: setting multi-level acquisition frequency, acquiring energy storage safety data such as cell temperature, module internal resistance, hydrogen concentration and the like in stages according to time intervals from large to small, and establishing a dynamic model based on an Arrhenius equation to predict theoretical internal resistance; the residual error rate of the actually measured internal resistance and the theoretical internal resistance is calculated to evaluate internal resistance abnormity, a risk index is generated by combining the hydrogen diffusion coefficient, the concentration change rate and internal resistance abnormity weighting, and then dynamic early warning measures are executed in a graded mode according to the risk threshold value corresponding to the cell temperature interval. According to the method, through a multi-parameter fusion analysis and dynamic response mechanism, early abnormality of the battery can be accurately captured, monitoring precision can be flexibly adjusted, risk levels can be quantified, differentiated disposal strategies can be matched, timeliness, comprehensiveness and pertinence of safety monitoring of the energy storage power station can be improved, the misjudgment rate can be effectively reduced, and the safety of the energy storage power station can be improved. And a scientific basis is provided for battery fault early warning and risk prevention and control.

Description

Technical Field

[0001] The present invention relates to the field of safety detection of energy storage power stations, and particularly to an active safety monitoring method for energy storage power stations. Background Art

[0002] With the large-scale application of energy storage power stations, battery safety accidents occur frequently. The traditional single-parameter monitoring mode is difficult to meet the early warning requirements. The existing technologies lack a dynamic monitoring mechanism and a multi-dimensional risk assessment system, and cannot accurately capture the internal performance decline and potential chemical reaction anomalies of batteries. Under this background, there is an urgent need for an active monitoring method that covers core parameters, integrates dynamic analysis and hierarchical response, so as to improve the timeliness and comprehensiveness of the safety prevention and control of energy storage power stations, and prevent safety hazards caused by monitoring lag or single evaluation.

[0003] The prior art, such as the invention patent application with the publication number CN119051261A, discloses a safety monitoring method and system for an energy storage power station. By monitoring the charging and discharging processes of all energy storage batteries in a target independent energy storage power station, and determining in real time whether there are abnormal charging and discharging behavior batteries. When it is determined that there are abnormal charging and discharging behavior batteries, the target connector image of the abnormal charging and discharging behavior battery is obtained, and whether there are obvious abnormal fault phenomena in the target connector is analyzed according to the target connector image. When the current flow inside the connector of the energy storage battery is abnormal, even if the obvious detachment or loosening phenomenon between the connector and the energy storage battery is excluded, a scientific and objective method can still be used to accurately analyze whether there is hidden loosening or detachment at the interface between the connector and the energy storage battery, and then the abnormal connection problem of the connector inside the energy storage power station can be accurately identified.

[0004] For the above solution, there are at least the following technical problems: 1. The above solution lacks the monitoring and analysis of the core performance parameters of energy storage batteries, which will lead to the inability to timely discover potential performance decline or fault hidden dangers inside the batteries. The internal resistance of the battery is a key indicator reflecting the health status of the battery. The abnormal change of the internal resistance of the battery indicates problems such as internal material aging and poor contact of the battery. If the internal resistance is not monitored and only the current transmission situation during charging and discharging is relied on to judge the battery abnormality, the early internal faults of the battery will be missed, and measures cannot be taken at the budding stage of the fault, ultimately leading to the rapid deterioration of the battery performance and even triggering safety accidents.

[0005] 2. The above solution does not set the data acquisition frequency in different stages, which will lead to the inability to flexibly adjust the monitoring accuracy according to the battery operation status. Batteries in different operation stages have different degrees of fault risk, and the required monitoring fineness should also be different. If the data is always collected at a single frequency, it will cause waste of resources due to excessive collection in the initial stage of battery operation, and in the stage when the battery is approaching a fault, the key subtle changes cannot be captured due to the low collection frequency, missing the best early warning opportunity, and greatly reducing the monitoring efficiency and effect.

[0006] 3. The above solution lacks the monitoring of gas indicators such as hydrogen concentration, which may lead to the failure to detect in a timely manner the abnormal chemical reactions that may occur inside the battery. Hydrogen is a product of energy storage batteries under certain fault conditions. For example, internal short circuits, thermal runaway, etc. inside the battery will generate hydrogen. If the hydrogen concentration is not monitored, it is impossible to judge the potential dangerous chemical reactions inside the battery during the rising stage of the gas concentration, and it is impossible to take measures such as ventilation and isolation in advance. Once the hydrogen concentration reaches the dangerous threshold, it is extremely easy to trigger serious accidents such as explosions, threatening the safety of the energy storage power station.

[0007] 4. The above solution lacks a mechanism for comprehensively evaluating the battery risk index based on multiple parameters and grading responses, which may lead to difficulties in accurately quantifying and specifically handling the battery safety risks. The battery safety risk is a result comprehensively affected by multiple factors. Judging only based on charge and discharge behaviors and connector failures cannot comprehensively evaluate the degree of risk faced by the battery. In addition, the lack of a risk index and grading responses makes it difficult to determine reasonable response strategies when facing abnormal situations of different degrees, and there may be over-reactions or under-reactions, which not only affect the normal operation efficiency of the energy storage power station but also cannot effectively ensure safety. Summary of the Invention

[0008] The purpose of the present invention is to provide an active safety monitoring method for an energy storage power station, which solves the problems existing in the background technology.

[0009] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an active safety monitoring method for an energy storage power station, including: S1. Set the acquisition frequencies of each level to collect data in stages, and then collect the energy storage safety data of the designated energy storage battery corresponding to the designated energy storage power station.

[0010] S2. According to the energy storage safety data of the designated energy storage battery corresponding to the designated energy storage power station, analyze the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the designated energy storage battery corresponding to the designated energy storage power station, and then evaluate whether there is a safety abnormality in the internal resistance.

[0011] S3. When there is a safety abnormality in the internal resistance, calculate the risk index of the designated energy storage battery at the corresponding time point when there is a safety abnormality in the internal resistance.

[0012] S4. Based on the risk index of the designated energy storage battery at the corresponding time point when there is a safety abnormality in the internal resistance, execute dynamic warning measures through graded responses.

[0013] The beneficial effects of the present invention are as follows: 1. For an active safety monitoring method of an energy storage power station provided by an embodiment of the present invention, during the data acquisition process, by setting multi-level acquisition frequencies and adjusting the acquisition time intervals in stages, such as initially using the maximum time interval and increasing the acquisition frequency when the cell temperature change exceeds the threshold, it is beneficial to optimize resource allocation while ensuring the effectiveness of monitoring. It not only avoids the waste of computing power and storage resources caused by high-frequency acquisition throughout the process, but also can automatically increase the monitoring density when the battery state tends to be abnormal, ensuring the capture of key data changes, improving the monitoring efficiency while reducing the system operation cost.

[0014] 2. During the internal resistance safety assessment process of an embodiment of the present invention, a dynamic model is constructed through the Arrhenius equation to predict the theoretical internal resistance, and the residual rate is introduced to calculate the deviation degree between the measured internal resistance and the theoretical value, which is beneficial to accurately identify the internal performance degradation of the battery. This model dynamically corrects the theoretical internal resistance by combining multiple factors such as temperature and current. Compared with the comparison of a single measured value, it is beneficial to more scientifically reflect the health state of the battery under different working conditions, avoiding misjudgment caused by environmental variable interference, and providing a quantitative basis for early detection of hidden faults such as internal material aging and poor contact of the battery.

[0015] 3. During the risk index calculation process of an embodiment of the present invention, by fusing multiple parameters such as the hydrogen diffusion coefficient, the hydrogen concentration change rate, and the internal resistance abnormality degree for weighted calculation, it is beneficial to comprehensively quantify the battery safety risk. Traditional single-parameter monitoring is difficult to comprehensively evaluate complex fault risks, while this method combines gas release characteristics with electrical performance indicators to construct a risk assessment system from both chemical reaction and physical performance dimensions, helping to more accurately reflect the potential danger degree inside the battery and avoiding risk misjudgment or omission caused by the limitation of a single index.

[0016] 4. During the dynamic early warning execution process of an embodiment of the present invention, by setting risk thresholds based on the cell temperature range and matching hierarchical response measures, such as implementing emergency isolation for high risks and strengthening monitoring for low risks, it is beneficial to achieve differential and precise prevention and control. This "monitoring - assessment - response" closed-loop mechanism can dynamically adjust the disposal strategy according to the risk level, quickly intervene in high-risk situations to avoid the spread of faults, and reasonably allocate resources for low-risk situations to prevent over-intervention from affecting the operation efficiency of the power station, improving the flexibility and pertinence of safety management.

[0017] 5. During the gas monitoring process of an embodiment of the present invention, by setting redundant hydrogen sensors at the top of the battery compartment, module monitoring points, and entrances and exits and performing mean value calculation, it is beneficial to improve the reliability of hydrogen concentration data. The redundant sensor design can reduce the impact of a single device failure on the monitoring result, and multi-point sampling combined with mean value processing can more truly reflect the gas distribution in the compartment, avoiding false alarms or monitoring blind spots caused by local concentration fluctuations, and providing more robust data support for judging abnormal chemical reactions inside the battery. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic diagram of the implementation steps of the present invention. Detailed Implementation Manner

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0021] Please refer to Figure 1 As shown, the present invention provides an active safety monitoring method for an energy storage power station, and the method includes: S1. Set the acquisition frequencies of each level to perform phased acquisition of data, and then acquire the energy storage safety data of the specified energy storage battery corresponding to the specified energy storage power station.

[0022] In a specific embodiment, the process of setting the acquisition frequencies of each level to perform phased acquisition of data is as follows: Before acquiring the energy storage safety data of the specified energy storage battery corresponding to the specified energy storage power station, set the acquisition frequencies of each level , is the number corresponding to the acquisition frequency of each level, The value of is a positive integer. Denote the number of the acquisition time interval corresponding to the acquisition frequency of each level as , then the acquisition time interval corresponding to the acquisition frequency of the th level is . Sort the acquisition frequencies of each level in descending order according to the corresponding time intervals. When the time interval of the th level acquisition frequency is the largest, then first acquire the energy storage safety data according to the th level acquisition frequency.

[0023] Based on the acquisition time interval corresponding to the th level acquisition frequency, collect the temperature value of the corresponding battery cell of the specified energy storage battery through the temperature sensors deployed on the surface of the specified energy storage battery. When , , then change the acquisition frequency to the acquisition frequency corresponding to the second largest time interval ranking, where For each acquisition time point corresponding to the level acquisition frequency, the value of is a positive integer, indicating the moment when the energy storage safety data acquisition starts according to the , and respectively represent the th acquisition time point and the cell temperature value corresponding to the

[0024] th acquisition time point. , It should be noted that the acquisition frequencies of each level refer to: for example, when there are three levels of acquisition frequencies, the first-level acquisition frequency is once every ten minutes, the second-level acquisition frequency is once every five minutes, and the third-level acquisition frequency is once every 30 seconds. First, the energy storage safety data is acquired using the first-level acquisition frequency. When refers to that if the current moment is the fourth acquisition time point and the current moment is ten o'clock, then is and and , then at the fourth acquisition time point, that is, at 10:00, the second-level acquisition frequency is used to acquire the energy storage safety data.

[0025] In a specific embodiment, the process of acquiring the energy storage safety data of the specified energy storage battery corresponding to the specified energy storage power station is as follows: The energy storage safety data includes cell temperature, module internal resistance, and hydrogen concentration. A DC internal resistance tester is connected in parallel to the positive and negative electrodes of the module corresponding to the specified energy storage battery. When the specified energy storage battery is not in the charging state or the discharging state, the internal resistance value corresponding to the specified energy storage battery at each acquisition time point is measured by the pulse current injection method.

[0026] A hydrogen gas sensor is installed at the top of the battery compartment corresponding to the specified energy storage battery. Monitoring points are set for every 3 modules of the specified energy storage battery, and redundant hydrogen gas sensors are set at the entrance and exit of the compartment. Then, the hydrogen concentration corresponding to each monitoring point at each acquisition time point is acquired. The average value of the hydrogen concentration corresponding to each monitoring point at each acquisition time point is calculated, and the result obtained is the hydrogen concentration corresponding to the specified energy storage battery at each acquisition time point.

[0027] In the internal resistance safety assessment process of the embodiments of the present invention, a dynamic model is constructed through the Arrhenius equation to predict the theoretical internal resistance, and the residual rate is introduced to calculate the deviation degree between the measured internal resistance and the theoretical value, which is beneficial to accurately identifying the internal performance degradation of the battery. This model dynamically corrects the theoretical internal resistance by combining multiple factors such as temperature and current. Compared with the comparison of a single measured value, it is beneficial to more scientifically reflect the health state of the battery under different working conditions, avoid misjudgment caused by environmental variable interference, and provide a quantitative basis for early detection of hidden faults such as internal material aging and poor contact of the battery.

[0028] S2. According to the energy storage safety data of the specified energy storage battery corresponding to the specified energy storage power station, analyze the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station, and then evaluate whether there is a safety anomaly in the internal resistance.

[0029] In a specific embodiment, the process of analyzing the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station is as follows: First, the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station is obtained through dynamic model prediction, and then the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station is calculated through the internal resistance relative error rate calculation formula.

[0030] In a specific embodiment, the process of obtaining the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station through dynamic model prediction is as follows: Select a comparative experimental battery of the same specification as the specified energy storage battery. Through the pulse current injection method, measure the reference internal resistance value corresponding to the comparative experimental battery. Set the temperature environment corresponding to a fixed temperature value, measure the internal resistance value of the comparative experimental battery in the temperature environment corresponding to the fixed temperature value, and measure the temperature value corresponding to the comparative experimental battery through a temperature sensor. Then substitute the reference internal resistance value, the internal resistance value in the temperature environment corresponding to the fixed temperature value, the temperature value, the gas constant, and the current ambient temperature into the linearized formula of the Arrhenius equation, with as the ordinate, representing the reference internal resistance value, and the reciprocal of the temperature as the abscissa to draw an image. Obtain the slope of the straight line through linear fitting, multiply the slope by the gas constant to obtain the activation energy , so as to obtain the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station through dynamic model prediction.

[0031] It should be noted that the temperature environment corresponding to a fixed temperature value refers to a certain constant temperature condition artificially set and controlled in the experiment, such as 25°C or 50°C, which is used to measure the internal resistance value of the battery at this stable temperature and exclude the interference of temperature fluctuations on the measurement results. For example, set the thermostat to 40°C and keep it stable, and measure the internal resistance of the battery in this environment to ensure that this temperature value is the only variable. The gas constant is a universal constant characterizing the properties of an ideal gas in thermodynamics, with a value of approximately 8.314 joules per mole per kelvin, which is used to describe the relationship between gas pressure, volume, amount of substance, and temperature. The linearized formula of the Arrhenius equation is prior art, and the exponential relationship is transformed into a linear relationship by taking the natural logarithm of the original equation, which will not be elaborated here.

[0032] It should also be noted that the expression corresponding to the dynamic model is as follows: , where 、 、 respectively represent the reference internal resistance, gas constant, and current influence coefficient corresponding to the specified energy storage battery, 、 respectively represent the current value and the theoretical predicted internal resistance of the specified energy storage battery at the th acquisition time point, takes the value of 2.71828.

[0033] The formula for calculating the relative error rate of the internal resistance is: , where represents the average internal resistance of the specified energy storage battery during the internal resistance acquisition process, represents the residual rate of the measured internal resistance and the theoretical predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station.

[0034] In a specific embodiment, the process of evaluating whether the internal resistance has a safety anomaly is as follows: Query the internal resistance safety residual rate threshold preset for the specified energy storage battery corresponding to the specified energy storage power station from the database, and compare the residual rate of the measured internal resistance and the theoretical predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station with the internal resistance safety residual rate threshold. If the residual rate of the measured internal resistance and the theoretical predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station is greater than the internal resistance safety residual rate threshold, it is determined that the internal resistance has a safety anomaly. If the residual rate of the measured internal resistance and the theoretical predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station is less than or equal to the internal resistance safety residual rate threshold, it is determined that the internal resistance does not have a safety anomaly.

[0035] It should be noted that the threshold of the internal resistance safety residual rate of the specified energy storage battery corresponding to the specified energy storage power station is used as the basis for evaluating whether there is a safety abnormality in the internal resistance. The threshold of the internal resistance safety residual rate is comprehensively set through historical data statistical analysis, battery manufacturer's specification sheets and industry standards. For example, the residual rate distribution of batteries of the same model under different working conditions is selected, and the mean plus 3 times the standard deviation is used as the threshold. If the residual rate of most batteries < 3%, the threshold is set to 5, or directly adopt the safety critical value recommended by the manufacturer. For example, the threshold of a certain lithium iron phosphate battery is 8%.

[0036] In the process of calculating the risk index in the embodiment of the present invention, by fusing multiple parameters such as the hydrogen diffusion coefficient, the hydrogen concentration change rate, and the internal resistance abnormality degree for weighted calculation, it is beneficial to comprehensively quantify the battery safety risk. It is difficult to comprehensively evaluate the complex fault risk by traditional single-parameter monitoring. This method combines the gas release characteristics with the electrical performance indicators, constructs a risk assessment system from the dual dimensions of chemical reaction and physical performance, helps to more accurately reflect the potential danger degree inside the battery, and avoids misjudgment or omission of risks caused by the limitations of a single indicator.

[0037] S3. When there is a safety abnormality in the internal resistance, calculate the risk index of the specified energy storage battery at the time point corresponding to the safety abnormality of the internal resistance.

[0038] In a specific embodiment, the process of calculating the risk index of the specified energy storage battery at the time point corresponding to the safety abnormality of the internal resistance is as follows: Denote the moment when the internal resistance of the current specified energy storage battery has a safety abnormality as , evaluate the hydrogen diffusion coefficient corresponding to the specified energy storage battery at the moment of through the gas diffusion formula, and denote the hydrogen diffusion coefficient as . According to the sampling frequency corresponding to the moment of , obtain the sampling time interval . By querying the internal hydrogen concentration corresponding to the specified energy storage battery at the moment of and the moment of , further obtain the hydrogen concentration increment between the specified energy storage battery at the moment of and the moment of . Combine to calculate the hydrogen concentration change rate corresponding to the specified energy storage battery at the moment of . Subtract the measured resistance collected at the moment of of the specified energy storage battery from the reference internal resistance corresponding to the fixed temperature value of the energy storage battery to calculate the internal resistance difference, and then divide the internal resistance difference by the reference internal resistance corresponding to the fixed temperature value of the energy storage battery to obtain the internal resistance abnormality degree. Then, perform weighted calculation on the hydrogen concentration change rate and the internal resistance abnormality degree corresponding to the specified energy storage battery at the moment of to obtain the risk index corresponding to the specified energy storage battery at the moment of .

[0039] It should be noted that the specific calculation formula for obtaining the hydrogen concentration change rate corresponding to the specified energy storage battery at moment is as follows: , is expressed as the hydrogen concentration change rate corresponding to the specified energy storage battery at moment, is expressed as the hydrogen concentration increment between the moment and the moment of the specified energy storage battery. The specific process of weighted calculation of the hydrogen concentration change rate and internal resistance abnormality corresponding to the specified energy storage battery at moment is as follows: , where is the risk index corresponding to the specified energy storage battery at moment, is expressed as the internal resistance abnormality, , are respectively the weight factors corresponding to the set internal resistance abnormality and hydrogen concentration increment, , both have a value range greater than 0 and less than 1. , The setting process is based on the sensitivity of risk parameters, the correlation of historical failure data, and industry standard experience. For example, if historical data shows that for every 10% increase in internal resistance abnormality, the failure probability increases by 25%, and for every increase in hydrogen concentration increment by more than 50, the failure probability increases by 15%, then a higher weight is given to the internal resistance abnormality, such as 0.6), and the weight of the hydrogen concentration increment is 0.4. , The setting process of

[0040] is similar to the weight allocation method of the multi-parameter safety assessment model in the prior art, such as the parameter importance ranking in the analytic hierarchy process or fault tree analysis. Therefore, the logic of the prior art can be referred to and will not be elaborated here. moment is as follows: When there is a safety abnormality in the internal resistance corresponding to the specified energy storage battery, based on the selected comparison experimental battery with the same specifications as the specified energy storage battery, the hydrogen diffusion coefficient corresponding to the comparison experimental battery under standard conditions is detected through a simulation experiment, and the corresponding current standard temperature and standard air pressure values under standard conditions are recorded. The core temperature and internal air pressure corresponding to the specified energy storage battery at moment are collected through a temperature sensor and a pressure sensor, and then the hydrogen diffusion coefficient corresponding to the specified energy storage battery at moment is calculated through the gas diffusion formula.

[0041] It should be noted that when detecting and comparing the hydrogen diffusion coefficient of the experimental battery under standard conditions through simulation experiments, the battery is first placed in a constant temperature and pressure environment, such as a laboratory environment with a standard temperature of 25 °C and a standard atmospheric pressure of 1 atm. The gas sensor is used to monitor the change in hydrogen concentration released by the battery during the charge and discharge process in real time. Combining with Fick's law, the hydrogen diffusion amount per unit time is calculated, so as to obtain the diffusion coefficient under standard conditions. For example, the hydrogen diffusion coefficient of a certain lithium iron phosphate battery is measured to be 0.2 square centimeters per second at 25 °C and 1 atm, and the current standard temperature and pressure values are recorded as reference data.

[0042] It should also be noted that the corresponding form of the gas diffusion formula is: , where represents the hydrogen diffusion coefficient corresponding to the specified energy storage battery at moment, represents the hydrogen diffusion coefficient corresponding to the experimental battery under standard conditions, , respectively represent the current standard temperature and standard atmospheric pressure values, , respectively represent the cell temperature and internal pressure corresponding to the specified energy storage battery at moment.

[0043] In the process of implementing the dynamic warning of this invention, by setting risk thresholds based on the cell temperature range and matching hierarchical response measures, such as implementing emergency isolation for high risks and strengthening monitoring for low risks, it is beneficial to achieve differential and precise prevention and control. This "monitoring - assessment - response" closed - loop mechanism can dynamically adjust the disposal strategy according to the risk level, quickly intervene in high - risk situations to avoid the spread of faults, reasonably allocate resources for low - risk situations, prevent over - intervention from affecting the operation efficiency of the power station, and improve the flexibility and pertinence of safety management.

[0044] S4. Based on the risk index of the specified energy storage battery at the time point corresponding to the safety anomaly due to the internal resistance, implement dynamic warning measures through hierarchical response.

[0045] In a specific embodiment, the process of implementing dynamic warning measures through hierarchical response is as follows: According to the cell temperature corresponding to the specified energy storage battery at moment, query the pre - set risk thresholds from the database. Each risk threshold corresponds to a cell temperature range. When the cell temperature collected by the specified energy storage battery at moment is within the cell temperature range corresponding to a certain risk threshold, then record this risk threshold as the target comparison risk threshold, and compare the target comparison risk threshold with the risk index corresponding to the specified energy storage battery at moment. If the risk index corresponding to the specified energy storage battery at If the risk index corresponding to the moment is greater than or equal to the target comparison risk threshold, it indicates that the specified energy storage battery has a high safety risk at the corresponding moment. If the specified energy storage battery has a risk index less than the target comparison risk threshold at the corresponding moment, it indicates that the specified energy storage battery has a low safety risk at the corresponding moment, and then the response measures corresponding to each risk are executed.

[0046] It should be noted that the preset risk thresholds are used to evaluate the risk levels corresponding to the anomalies when the specified energy storage battery has abnormal internal resistance. The risk thresholds are comprehensively set through the battery's thermodynamic characteristics, manufacturer's safety specifications, and industry standards. First, the critical temperature of thermal runaway is determined based on the thermal stability of the battery material. For example, the starting temperature of thermal runaway of a certain ternary lithium battery is about 210°C. Then, a safety margin is reserved in combination with the charging and discharging conditions. For example, the high-temperature threshold during charging is set to 60°C, the low-temperature threshold is set to 0°C, and the high-temperature threshold during discharging is set to 55°C. At the same time, the abnormal temperature range in the historical fault data is referred to. For example, it is statistically found that the failure probability increases sharply when the cell temperature > 65°C, and the warning threshold is set to 60°C, and finally, each risk threshold is formed.

[0047] In a specific embodiment, the process of executing the response measures corresponding to each risk is as follows: Query the high-risk response measures and low-risk response measures corresponding to the target comparison risk threshold from the database. When the specified energy storage battery has a high safety risk at the corresponding moment, the high-risk response measures are executed. When the specified energy storage battery has a low safety risk at the corresponding moment, the low-risk response measures are executed.

[0048] It should be noted that, for example, when the specified energy storage battery has a high safety risk at the corresponding moment, the high-risk response measures include: immediately cutting off the charging and discharging circuit of the battery cluster, starting the full-cabin gas fire extinguishing system, sending a shutdown signal to the grid dispatching and triggering a remote operation and maintenance work order. If the specified energy storage battery has a low safety risk at the corresponding moment, the low-risk response measures are: turning on local air-cooled heat dissipation, reducing the charging and discharging power by 10%, and increasing the data acquisition frequency of the sensor, such as increasing it from 10 seconds / time to 5 seconds / time.

[0049] In the gas monitoring process of the embodiments of the present invention, by setting redundant hydrogen sensors at the top of the battery compartment, the module monitoring points and the entrances and exits and calculating the mean value, it is beneficial to improve the reliability of the hydrogen concentration data. The redundant sensor design can reduce the impact of a single device failure on the monitoring result. The multi-point sampling combined with the mean value processing can more truly reflect the gas distribution in the compartment, avoid false alarms or monitoring blind spots caused by local concentration fluctuations, and provide more robust data support for judging the abnormal chemical reaction inside the battery.

[0050] A database is used to store the pre-set internal resistance safety residual rate threshold corresponding to the specified energy storage battery of the specified energy storage power station, and also stores the pre-set risk thresholds, each risk threshold corresponding to a cell temperature range, and also stores the high-risk response measures and low-risk response measures corresponding to the target comparison risk threshold.

[0051] In the data acquisition process of the active safety monitoring method for an energy storage power station provided by the embodiments of the present invention, by setting multi-level acquisition frequencies and adjusting the acquisition time intervals in stages, such as initially using the maximum time interval and increasing the acquisition frequency when the cell temperature change exceeds the threshold, it is beneficial to optimize the resource allocation while ensuring the monitoring effectiveness. It not only avoids the waste of computing power and storage resources caused by high-frequency acquisition throughout the process, but also can automatically increase the monitoring density when the battery state tends to be abnormal, ensuring that key data changes are captured, improving the monitoring efficiency and reducing the system operation cost at the same time.

[0052] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements or use similar methods to replace the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by this specification, they should all fall within the protection scope of the present invention.

Claims

1. An active safety monitoring method for an energy storage power station, characterized in that Including: S1. Set the acquisition frequencies of each level to conduct phased acquisition of data, and then acquire the energy storage safety data of the specified energy storage battery corresponding to the specified energy storage power station; S2. According to the energy storage safety data of the specified energy storage battery corresponding to the specified energy storage power station, analyze the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station, and then evaluate whether there is a safety anomaly in the internal resistance; S3. When there is a safety anomaly in the internal resistance, calculate the risk index of the specified energy storage battery at the corresponding time point when the internal resistance has a safety anomaly; S4. Based on the risk index of the specified energy storage battery at the corresponding time point when the internal resistance has a safety anomaly, execute dynamic warning measures through hierarchical response.

2. The active safety monitoring method for an energy storage power station according to claim 1, characterized in that, The process of setting the acquisition frequencies of each level to conduct phased acquisition of data is as follows: Before collecting the energy storage safety data of the specified energy storage battery corresponding to the specified energy storage power station, set the collection frequencies at each level , takes a positive integer value, and record the collection time interval corresponding to each level of collection frequency as , sort the collection frequencies at each level in descending order according to the corresponding time intervals. When the time interval of the -th level collection frequency is the largest, first collect the energy storage safety data according to the -th level collection frequency; Based on the collection time interval corresponding to the -level collection frequency, the temperature value of the corresponding battery cell of the specified energy storage battery is collected by the temperature sensor deployed on the surface of the specified energy storage battery. When , When, is the time interval corresponding to the -level collection frequency, the collection frequency is changed to the collection frequency corresponding to the second in the time interval ranking, where is the -level collection frequency corresponding to each collection time point, takes positive integer values, represents the moment when the energy storage safety data collection starts according to the -level collection frequency, , respectively represent the th collection time point and the th collection time point corresponding to the battery cell temperature value.

3. The active safety monitoring method for an energy storage power station according to claim 2, characterized in that, The process of acquiring the energy storage safety data of the specified energy storage battery corresponding to the specified energy storage power station is as follows: The energy storage safety data includes the cell temperature, module internal resistance, and hydrogen concentration. Connect the DC internal resistance tester in parallel to the positive and negative poles of the corresponding module of the specified energy storage battery. When the specified energy storage battery is not in the charging state or the discharging state, measure the internal resistance value of the specified energy storage battery at each acquisition time point through the pulse current injection method; Install a hydrogen gas sensor on the top of the battery compartment corresponding to the specified energy storage battery. Set monitoring points for every 3 modules of the specified energy storage battery, and install redundant hydrogen gas sensors at the entrance and exit of the compartment. Then acquire the hydrogen concentration at each monitoring point corresponding to each acquisition time point, and calculate the average value of the hydrogen concentration at each monitoring point corresponding to each acquisition time point. The result obtained is the hydrogen concentration of the specified energy storage battery at each acquisition time point.

4. The active safety monitoring method for an energy storage power station according to claim 3, wherein The process of analyzing the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station is as follows: First, predict the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station through a dynamic model, and then calculate the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station through the internal resistance relative error rate calculation formula.

5. The active safety monitoring method for an energy storage power station according to claim 4, wherein The process of predicting the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station through a dynamic model is as follows: Select a comparative experimental battery with the same specifications as the specified energy storage battery. By the pulse current injection method, measure the reference internal resistance value corresponding to the comparative experimental battery. Set the temperature environment corresponding to the fixed temperature value, and measure the internal resistance value of the comparative experimental battery in the temperature environment corresponding to the fixed temperature value. Measure the temperature value corresponding to the comparative experimental battery through a temperature sensor, and then substitute the reference internal resistance value, the internal resistance value in the temperature environment corresponding to the fixed temperature value, the temperature value, the gas constant, and the current ambient temperature into the linearized formula of the Arrhenius equation so as to be the ordinate, represent the reference internal resistance value, and plot an image with the reciprocal of the temperature as the abscissa. Obtain the slope of the straight line through linear fitting, multiply the slope by the gas constant to obtain the activation energy , and thus theoretically predict the internal resistance of the specified energy storage battery corresponding to the specified energy storage power station through the dynamic model.

6. The active safety monitoring method for an energy storage power station according to claim 5, characterized in that, The process of evaluating whether there is a safety anomaly in the internal resistance is as follows: Query the pre-set internal resistance safety residual rate threshold of the specified energy storage battery corresponding to the specified energy storage power station from the database. Compare the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station with the internal resistance safety residual rate threshold. If the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station is greater than the internal resistance safety residual rate threshold, it is determined that there is a safety anomaly in the internal resistance. If the residual rate of the measured internal resistance and the theoretically predicted internal resistance of the specified energy storage battery corresponding to the specified energy storage power station is less than or equal to the internal resistance safety residual rate threshold, it is determined that there is no safety anomaly in the internal resistance.

7. The active safety monitoring method for an energy storage power station according to claim 6, wherein The process of calculating the risk index of the specified energy storage battery at the corresponding time point when the internal resistance has a safety anomaly is as follows: Record the moment when the internal resistance of the currently specified energy storage battery has a safety anomaly as , and evaluate the hydrogen diffusion coefficient corresponding to the specified energy storage battery at using the gas diffusion formula, and record the hydrogen diffusion coefficient as . According to the acquisition frequency corresponding to the moment, obtain the acquisition time interval . By querying the internal hydrogen concentration corresponding to the specified energy storage battery at and moments, and then obtain the hydrogen concentration increment of the specified energy storage battery between and moments. Combine and calculate the hydrogen concentration change rate corresponding to the specified energy storage battery at accordingly. Subtract the measured resistance obtained by collecting the specified energy storage battery at from the reference internal resistance corresponding to the fixed temperature value of the energy storage battery to calculate the internal resistance difference, and then divide the internal resistance difference by the reference internal resistance corresponding to the fixed temperature value of the energy storage battery to obtain the internal resistance anomaly degree. Then, perform a weighted calculation on the hydrogen concentration change rate and the internal resistance anomaly degree corresponding to the specified energy storage battery at to obtain the risk index corresponding to the specified energy storage battery at moment.

8. The active safety monitoring method for an energy storage power station according to claim 7, wherein Evaluating the hydrogen diffusion coefficient corresponding to a specified energy storage battery at by the gas diffusion formula, and the specific process is as follows: When there is a safety anomaly in the internal resistance of the specified energy storage battery, based on the selected comparative experimental battery of the same specification as the specified energy storage battery, the hydrogen diffusion coefficient corresponding to the comparative experimental battery under standard conditions is obtained through simulation experiments, and the corresponding current standard temperature and standard atmospheric pressure value under standard conditions are recorded, and the core temperature and internal atmospheric pressure of the specified energy storage battery at the corresponding moment are collected through a temperature sensor and an atmospheric pressure sensor, and then the hydrogen diffusion coefficient corresponding to the specified energy storage battery at the corresponding moment is calculated through the gas diffusion formula.

9. The active safety monitoring method for an energy storage power station according to claim 8, characterized in that, The process of executing dynamic warning measures through hierarchical response is as follows: According to the cell temperature corresponding to the specified energy storage battery at moment, query the preset risk thresholds from the database. Each risk threshold corresponds to a cell temperature range. When the cell temperature collected by the specified energy storage battery at moment is within the cell temperature range corresponding to a certain risk threshold, then record this risk threshold as the target comparison risk threshold, and compare the target comparison risk threshold with the risk index corresponding to the specified energy storage battery at moment. If the risk index corresponding to the specified energy storage battery at moment is greater than or equal to the target comparison risk threshold, it indicates that the safety risk corresponding to the specified energy storage battery at moment is a high risk. If the risk index corresponding to the specified energy storage battery at moment is less than the target comparison risk threshold, it indicates that the safety risk corresponding to the specified energy storage battery at moment is a low risk, and then implement the response measures corresponding to each risk.

10. The active safety monitoring method for an energy storage power station according to claim 9, wherein The process of executing the response measures corresponding to each risk is as follows: Query the high-risk response measures and low-risk response measures corresponding to the target comparison risk threshold from the database. When the safety risk corresponding to the specified energy storage battery at is a high risk, then execute the high-risk response measures. When the safety risk corresponding to the specified energy storage battery at is a low risk, then execute the low-risk response measures.

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