Reliability evaluation method and system of energy storage system

Abstract

The invention discloses a reliability evaluation method and system for an energy storage system. The method comprises the following steps: periodically acquiring operation data of the energy storage system and environment characteristic data of an environment where the energy storage system is located; extracting reliability index data based on the operation data, and obtaining a reliability standard degree coefficient according to the reliability index data; inputting the environment characteristic data into a target environment factor interference evaluation model to obtain an environment interference coefficient; optimizing the reliability standard degree coefficient based on the environment interference coefficient to obtain a target reliability coefficient of the energy storage system; determining whether the target reliability coefficient satisfies a reliability condition; and if the target reliability coefficient does not meet the reliability condition, generating alarm information and giving an alarm. According to the method, the influence of the environment on the reliability is considered, and the target reliability coefficient is obtained through comprehensive quantitative evaluation of the reliability index, so that the accuracy of the reliability evaluation result of the energy storage system is improved.

Description

Technical Field

[0001] This invention relates to the field of reliability assessment technology, and in particular to a reliability assessment method and system for energy storage systems. Background Technology

[0002] With the continuous development of new energy sources, the proportion of new energy power plants connected to the grid is constantly increasing. Energy storage systems are becoming increasingly important due to their advantages in smoothing fluctuations in new energy power generation, improving the dispatchability of new energy sources, supporting grid frequency and voltage stability, and providing backup support and fault response. Therefore, it is necessary to ensure the reliability of energy storage systems to guarantee the stable operation of power plants.

[0003] In related technologies, the reliability of energy storage systems is evaluated based on the performance indicators of the energy storage system itself, which results in low accuracy of the reliability evaluation results. Furthermore, the lack of comprehensive quantitative evaluation of reliability indicators and effective prediction and handling of potential risks during reliability evaluation leads to low accuracy of reliability evaluation results, making it difficult to meet the needs of practical applications. Summary of the Invention

[0004] The reliability assessment method and system for energy storage systems proposed in this invention aim to solve the technical problem of low accuracy of reliability assessment results in the aforementioned related technologies.

[0005] To achieve the above objectives, the present invention provides a reliability assessment method for an energy storage system, the method comprising: Periodically acquire operational data of the energy storage system and environmental characteristic data of the environment in which the energy storage system is located; Reliability index data is extracted based on the operational data, and a reliability compliance coefficient is obtained based on the reliability index data. The environmental characteristic data is input into the target environmental factor interference assessment model to obtain the environmental interference coefficient; The reliability compliance coefficient is optimized based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system. Determine whether the target reliability coefficient meets the reliability conditions; if the target reliability coefficient does not meet the reliability conditions, generate alarm information and issue an alarm.

[0006] The reliability assessment method for the energy storage system according to embodiments of the present invention may also have the following additional technical features: In one embodiment of the present invention, the step of extracting reliability index data based on the operational data and obtaining a reliability compliance coefficient based on the reliability index data includes: Extract reliability index data from the operational data; The corresponding reliability data is obtained by calculating based on the reliability index data and the corresponding preset index threshold. The reliability data is weighted and summed to obtain the reliability compliance coefficient.

[0007] In one embodiment of the present invention, extracting reliability index data from the operational data includes: The operating data of the energy storage system is divided into multiple candidate data, and multiple reliability index parameters are extracted from each candidate data, wherein each reliability index parameter is a parameter time series. Calculate the reliability of each of the aforementioned reliability index parameters; Based on the reliability of each of the aforementioned reliability index parameters, the reliability index data in the operational data is determined.

[0008] In one embodiment of the present invention, calculating the reliability of each of the reliability index parameters includes: Calculate the coupling reliability of each of the aforementioned reliability index parameters; Calculate the numerical reliability of each of the reliability index parameters; Based on the coupling reliability and the numerical reliability of each reliability index parameter, the parameter reliability of each reliability index parameter is determined.

[0009] In one embodiment of the present invention, calculating the coupling reliability of each of the reliability index parameters includes: Calculate the correlation of the amount of change between any two reliability index parameters in each set of candidate data; Based on the correlation of the changes, calculate the coupling degree between any two reliability index parameters; Based on the coupling degree, the coupling reliability of each reliability index parameter is calculated.

[0010] In one embodiment of the present invention, calculating the numerical reliability of each of the reliability index parameters includes: Calculate the stability of each of the aforementioned reliability index parameters; The numerical reliability of each reliability index parameter is calculated based on the stability of each reliability index parameter.

[0011] In one embodiment of the present invention, optimizing the reliability compliance coefficient based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system includes: optimizing the reliability compliance coefficient based on the environmental interference coefficient using an optimization formula to obtain the target reliability coefficient of the energy storage system, wherein the optimization formula is:

[0012] in, The target reliability coefficient, The reliability compliance coefficient, As a moderating factor of environmental impact, This represents the environmental interference coefficient.

[0013] In one embodiment of the present invention, determining whether the target reliability coefficient satisfies the reliability condition includes: If the target reliability coefficient is greater than the preset reliability value, then the reliability condition is determined to be met. If the target reliability coefficient is less than or equal to the preset reliability value, then the reliability condition is determined not to be met.

[0014] To achieve the above objectives, another aspect of the present invention proposes a reliability assessment system for an energy storage system, the system comprising: The acquisition module periodically acquires the operating data of the energy storage system and the environmental characteristic data of the environment in which the energy storage system is located; The first data processing module is used to extract reliability index data based on the operation data, and to obtain the reliability compliance coefficient based on the reliability index data. The second data processing module is used to input the environmental characteristic data into the target environmental factor interference assessment model to obtain the environmental interference coefficient. The optimization module is used to optimize the reliability compliance coefficient based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system. The determination module is used to determine whether the target reliability coefficient meets the reliability conditions; An alarm module is used to generate alarm information and issue an alarm if the target reliability coefficient does not meet the reliability conditions.

[0015] Another object of the present invention is to provide an electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of the first aspects above.

[0016] Another object of the present invention is to provide a computer storage medium storing computer-executable instructions; the computer-executable instructions, when executed by a processor, cause the computer to perform the method described in any one of the first aspects above.

[0017] The reliability assessment method and system for energy storage systems according to embodiments of the present invention periodically acquire operational data of the energy storage system and environmental characteristic data of the environment in which the energy storage system is located; extract reliability index data based on the operational data, and obtain a reliability compliance coefficient based on the reliability index data; input the environmental characteristic data into a target environmental factor interference assessment model to obtain an environmental interference coefficient; optimize the reliability compliance coefficient based on the environmental interference coefficient to obtain a target reliability coefficient for the energy storage system; determine whether the target reliability coefficient meets the reliability conditions; if the target reliability coefficient does not meet the reliability conditions, generate alarm information and issue an alarm. This invention obtains the environmental interference coefficient through environmental characteristic data of the environment in which the energy storage system is located, and uses the environmental interference coefficient to optimize the reliability compliance coefficient obtained from the operational data of the energy storage system to obtain the target reliability coefficient. It considers the impact of the environment on its reliability, and obtains the target reliability coefficient through a comprehensive quantitative assessment of reliability indicators, thereby improving the accuracy of the reliability assessment results of the energy storage system.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a reliability assessment method for an energy storage system according to an embodiment of the present invention; Figure 2 This is a structural diagram of a reliability assessment system for an energy storage system according to an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] The following describes, with reference to the accompanying drawings, a reliability assessment method and system for energy storage systems proposed according to embodiments of the present invention.

[0023] Figure 1 This is a flowchart of a reliability assessment method for an energy storage system according to an embodiment of the present invention.

[0024] like Figure 1 As shown, the method includes: S1 periodically acquires operational data of the energy storage system and environmental characteristic data of the environment in which the energy storage system is located.

[0025] In one embodiment of the present invention, as the importance of energy storage systems in grid operation increases, accurate reliability assessment of energy storage systems becomes increasingly crucial for timely maintenance and ensuring their normal and stable operation. However, existing methods for assessing the reliability of energy storage systems rely solely on operational data during system operation, neglecting the influence of the operating environment, leading to reduced accuracy in the assessment results. Therefore, the present invention periodically acquires environmental characteristic data of the environment in which the energy storage system operates. This environmental characteristic data may include temperature, humidity, battery interference, and vibration amplitude.

[0026] Furthermore, in one embodiment of the present invention, environmental characteristic data of the environment in which the energy storage system is located can be obtained through pre-installed sensors.

[0027] Furthermore, in one embodiment of the present invention, the operating data of the energy storage system may include inter-battery pressure data, battery response time, battery discharge power, battery charging power, battery deformation data, and capacitor power supply time.

[0028] In one embodiment of the present invention, the aforementioned inter-battery pressure data may be inter-battery pressure data within multiple energy storage systems or gas pressure data at a specified location; the aforementioned battery response time may be the response time of a battery unit after the control system issues an instruction to any working unit such as a battery unit; the aforementioned battery discharge power may be the discharge power of any battery unit in the charging stage; and the aforementioned battery deformation data may be deformation data of the battery appearance obtained through image processing technology, or deformation data of important components.

[0029] S2 extracts reliability index data based on operational data and obtains the reliability compliance coefficient based on the reliability index data.

[0030] In one embodiment of the present invention, after obtaining the operating data through the above steps, reliability index data can be extracted based on the operating data, and a reliability compliance coefficient can be obtained based on the reliability index data.

[0031] In one embodiment of the present invention, the method for extracting reliability index data based on operational data and obtaining a reliability compliance coefficient based on the reliability index data may include the following steps: S21, Extract reliability index data from the operational data.

[0032] In one embodiment of the present invention, the method for extracting reliability index data from operational data may include the following steps: S211, the operating data of the energy storage system is divided into multiple candidate data, and multiple reliability index parameters are extracted from each candidate data, wherein each reliability index parameter is a parameter time series.

[0033] In one embodiment of the present invention, during the above-mentioned extraction of operating data, the periodically collected operating data is divided into multiple candidate data in equal proportions, and then the reliability index parameters in each candidate data are extracted, so that the extracted reliability index data can more accurately reflect the current status of the energy storage system.

[0034] In one embodiment of the present invention, the above-mentioned reliability index parameters are time series parameters.

[0035] S212, calculate the reliability of each reliability index parameter.

[0036] In one embodiment of the present invention, the method for calculating the reliability of each reliability index parameter may include the following steps: S2121, calculate the coupling reliability of each reliability index parameter; S2122, calculate the numerical reliability of each reliability index parameter; S2123, based on the coupling reliability and numerical reliability of each reliability index parameter, determine the parameter reliability of each reliability index parameter.

[0037] In one embodiment of the present invention, the reliability of the aforementioned parameters can be composed of coupling reliability and numerical reliability. Specifically, in one embodiment, coupling reliability is obtained through time-shifted calculation to determine the coupling degree between any two different parameters, thereby verifying the reliability of the candidate data. For example, due to factors such as thermal inertia, battery temperature is related to both battery discharge power and the heat dissipation system; their changes are correlated and have a certain time delay. Time-shifted calculation is used to obtain the coupling degree between any two different parameters to determine the accuracy of the data.

[0038] Specifically, in one embodiment of the present invention, the method for calculating the coupling reliability of each reliability index parameter may include the following steps: Step 1: Calculate the correlation of changes between any two reliability index parameters in each set of candidate data.

[0039] In one embodiment of the present invention, the method for calculating the correlation of changes between any two reliability index parameters in each set of candidate data may include: calculating the correlation of changes between any two reliability index parameters in each set of candidate data using a first formula, wherein the first formula is:

[0040] in, Indicates the first data in the candidate data. The reliability index parameter and the first The correlation of changes in the reliability index parameters Indicates the preset delay. Indicates the first data in the candidate data. The reliability index parameter is at the 1st The change at each sampling time Indicates the first data in the candidate data. The reliability index parameter is at the 1st The change at each sampling time This indicates the preset sliding length.

[0041] In one embodiment of the present invention, the correlation of changes obtained through the above steps takes into account the time delay of data changes, and can capture the correlation between the changes of different parameters at different times. For example, in the operation of an energy storage system, after the power of the heat dissipation system changes, it may take a time delay to cause changes in parameters such as battery temperature. The above-mentioned correlation of changes can effectively characterize the correlation of dynamic lag, and compared with the correlation calculation without considering the time delay, it can more accurately reflect the true correlation between the reliability index parameters of the energy storage system. Furthermore, the product of the changes of two reliability indices is used to reflect the same direction between the two reliability index parameters, that is, when the two parameters change in the same direction, their product result is positive, and vice versa. It should be noted that the above-mentioned changes are obtained by subtracting time series data.

[0042] Step 2: Calculate the coupling degree between any two reliability index parameters based on the correlation of changes.

[0043] In one embodiment of the present invention, the method for calculating the coupling degree between any two reliability index parameters based on the correlation of changes may include: calculating the coupling degree between any two reliability index parameters using a second formula based on the correlation of changes, wherein the second formula is:

[0044] in, Indicates the first data in the candidate data. The reliability index parameter and the first The degree of coupling between the reliability index parameters Indicates the first data in the candidate data. The reliability index parameter and the first The product of the standard deviations of the changes in each reliability index parameter This represents a preset constant. This function represents the maximum value and is used to obtain the th element in the candidate data. The reliability index parameter is at the 1st The change at the sampling time and the first sampling time The correlation of changes at each sampling time point.

[0045] Step 3: Calculate the coupling reliability of each reliability index parameter based on the coupling degree.

[0046] In one embodiment of the present invention, the method for calculating the coupling reliability of each reliability index parameter based on the coupling degree may include: calculating the coupling reliability of each reliability index parameter using a third formula based on the coupling degree, wherein the third formula is:

[0047] in, Indicates the first data in the candidate data. The reliability of the coupling of several reliability index parameters This indicates the number of reliability index parameters in the candidate data.

[0048] In one embodiment of the present invention, by calculating the coupling reliability, under a preset time delay, the higher the coupling reliability, the more accurate the data will be, and the more related the reliability index data will also change when the data of a reliability index changes.

[0049] Furthermore, in one embodiment of the present invention, data credibility reflects the degree of credibility of the data through changes in the data itself.

[0050] In one embodiment of the present invention, the method for calculating the numerical reliability of each reliability index parameter may include the following steps: Step a: Calculate the stability of each reliability index parameter.

[0051] In one embodiment of the present invention, the method for calculating the stability of each reliability index parameter may include: calculating the stability of each reliability index parameter using a fourth formula, wherein the fourth formula is:

[0052] in, The first in the candidate data The stability of each reliability index parameter It is the first in the candidate data The total number of time series data intervals corresponding to each reliability index parameter Indicates the first data in the candidate data. The time series corresponding to the reliability index parameter is the first... The variance of all parameters within a data interval. Indicates the first data in the candidate data. The variance of the time series of each reliability index parameter.

[0053] In one embodiment of the present invention, in the fourth formula for calculating stability, the variance of all parameters within the data interval of the time series corresponding to the reliability index parameter and the stability of the fluctuation of all reliability index parameters within the time series corresponding to the parameter are used. When the variance of all data intervals of the reliability index parameter in the candidate data is close to the time series variance of the reliability index parameter, it indicates that the fluctuation stability of the data of the reliability index parameter is higher, and the reliability of its parameter value is also higher. In one embodiment of the present invention, the aforementioned data interval can be defined as a data interval formed by expanding forward and backward from a certain data point during the data selection process, selecting a preset number of data points, and then using all the selected data as a single data interval.

[0054] Step b: Calculate the numerical reliability of each reliability index parameter based on the stability of each reliability index parameter.

[0055] In one embodiment of the present invention, the method for calculating the numerical reliability of each reliability index parameter based on the stability of each reliability index parameter may include: calculating the numerical reliability of each reliability index parameter using a fifth formula based on the stability of each reliability index parameter, wherein the fifth formula is:

[0056] in, The first in the candidate data The reliability of the numerical values ​​of each reliability index parameter.

[0057] In one embodiment of the present invention, by calculating the stability of the data fluctuation in the candidate data, the reliability index data can be screened to improve the degree to which the reliability index data truly reflects the condition of the energy storage system and improve the accuracy of the reliability judgment of the energy storage system.

[0058] Furthermore, in one embodiment of the present invention, after obtaining the coupling reliability and numerical reliability of each reliability index parameter through the above steps, the parameter reliability of each reliability index parameter can be determined based on the coupling reliability and numerical reliability of each reliability index parameter.

[0059] Specifically, in one embodiment of the present invention, the method for determining the parameter reliability of each reliability index parameter based on the coupled reliability and numerical reliability of each reliability index parameter may include: determining the parameter reliability of each reliability index parameter using a sixth formula based on the coupled reliability and numerical reliability of each reliability index parameter, wherein the sixth formula is:

[0060] in, Indicates the first The reliability of each reliability index parameter. Indicates the first data in the candidate data. The reliability of the coupling of several reliability index parameters Indicates the first data in the candidate data. The reliability of the numerical values ​​of each reliability index parameter.

[0061] In one embodiment of the present invention, the product of data reliability and coupling reliability is used as the parameter reliability, which can reflect the reliability of the reliability index parameters in the candidate data. Furthermore, in another embodiment of the present invention, the higher the parameter reliability of the aforementioned reliability index parameters, the lower the error probability of the reliability index parameters corresponding to the candidate data, and the better it reflects the true situation of the current energy storage system.

[0062] S213, Based on the reliability of each reliability index parameter, determine the reliability index data in the operational data.

[0063] In one embodiment of the present invention, after obtaining the parameter credibility of each reliability index parameter through the above steps, the reliability index data in the operation data can be determined based on the parameter credibility of each reliability index parameter.

[0064] In one embodiment of the present invention, the reliability index parameters can be sorted in descending order of their reliability levels, and a predetermined number of the top-ranked reliability index parameters can be determined as the reliability index data in the operational data. In one embodiment of the present invention, the predetermined number can be set as needed, such as 5.

[0065] S22, calculate the corresponding reliability data based on the reliability index data and the corresponding preset index threshold.

[0066] In one embodiment of the present invention, after obtaining the reliability index data through the above steps, the reliability index data and the corresponding preset index threshold can be calculated to obtain the corresponding reliability data.

[0067] In one embodiment of the present invention, if the reliability index data is a positive indicator (such as capacitor power supply time), that is, the larger the index value, the higher the reliability, then the reliability index data and the corresponding preset index threshold are calculated using the seventh formula to obtain the corresponding reliability data, wherein the seventh formula is:

[0068] in, For the reliability data corresponding to the i-th reliability index data, The preset threshold corresponding to the i-th reliability index data. This is the data for the i-th reliability metric.

[0069] In one embodiment of the present invention, It can be the average value of the time series of the parameter corresponding to the i-th reliability index data.

[0070] Furthermore, in one embodiment of the present invention, if the reliability index data is a negative index (such as capacitor power supply time abnormality rate), that is, the smaller the index value, the higher the reliability, then the reliability index data and the corresponding preset index threshold are calculated using the eighth formula to obtain the corresponding reliability data, wherein the eighth formula is:

[0071] in, For the reliability data corresponding to the i-th reliability index data, The preset threshold corresponding to the i-th reliability index data. This represents the i-th reliability metric data.

[0072] S23, perform weighted summation on the reliability data to obtain the reliability compliance coefficient.

[0073] In one embodiment of the present invention, after obtaining reliability data through the above steps, the reliability data can be weighted and summed to obtain the reliability compliance coefficient.

[0074] In one embodiment of the present invention, the weight coefficients corresponding to the above-mentioned reliability data can be preset based on experience, or the weight coefficients corresponding to each reliability data can be determined based on methods such as multiple regression analysis.

[0075] S3. Input the environmental characteristic data into the target environmental factor interference assessment model to obtain the environmental interference coefficient.

[0076] In one embodiment of the present invention, after obtaining environmental characteristic data through the above steps, the environmental characteristic data can be input into the target environmental factor interference assessment model to obtain the environmental interference coefficient.

[0077] In one embodiment of the present invention, the above-mentioned target environmental factor interference assessment model can be based on a large amount of experimental data and industry standards, treating multiple environmental characteristic data as independent variables, and determining the influence weight of each environmental characteristic data on the operational reliability of the energy storage system through methods such as multiple regression analysis, and weighting and summing the different environmental characteristic data according to their influence weights to obtain the environmental interference coefficient.

[0078] S4. Based on the environmental interference coefficient, the reliability compliance coefficient is optimized to obtain the target reliability coefficient of the energy storage system.

[0079] In one embodiment of the present invention, during actual operation, environmental factors affect the performance of the energy storage system and the new energy power station due to their long-term use together. Therefore, after determining the environmental interference coefficient and reliability compliance coefficient through the above steps, the reliability compliance coefficient can be optimized based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system.

[0080] In one embodiment of the present invention, the method for optimizing the reliability compliance coefficient based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system may include: optimizing the reliability compliance coefficient based on the environmental interference coefficient using an optimization formula to obtain the target reliability coefficient of the energy storage system, wherein the optimization formula is:

[0081] in, The target reliability coefficient, The reliability compliance coefficient, As a moderating factor of environmental impact, This represents the environmental interference coefficient.

[0082] S5, determine whether the target reliability coefficient meets the reliability conditions.

[0083] In one embodiment of the present invention, after determining the target reliability coefficient through the above steps, it can be determined whether the target reliability coefficient meets the reliability conditions.

[0084] In one embodiment of the present invention, the method for determining whether the target reliability coefficient meets the reliability condition may include: if the target reliability coefficient is greater than a preset reliability value, then it is determined that the reliability condition is met; if the target reliability coefficient is less than or equal to the preset reliability value, then it is determined that the reliability condition is not met.

[0085] S6. If the target reliability coefficient does not meet the reliability conditions, an alarm message is generated and an alarm is issued.

[0086] In one embodiment of the present invention, if the target reliability coefficient meets the reliability condition, it indicates that the energy storage system is reliable and no alarm is required.

[0087] The reliability assessment method for energy storage systems in this invention periodically acquires operational data and environmental characteristic data of the energy storage system's environment; extracts reliability index data based on the operational data and obtains a reliability compliance coefficient based on the reliability index data; inputs the environmental characteristic data into a target environmental factor interference assessment model to obtain an environmental interference coefficient; optimizes the reliability compliance coefficient based on the environmental interference coefficient to obtain a target reliability coefficient for the energy storage system; determines whether the target reliability coefficient meets the reliability conditions; if the target reliability coefficient does not meet the reliability conditions, an alarm message is generated and an alarm is issued. This invention obtains the environmental interference coefficient from the environmental characteristic data of the energy storage system's environment and uses the environmental interference coefficient to optimize the reliability compliance coefficient obtained from the energy storage system's operational data to obtain the target reliability coefficient. It considers the impact of the environment on its reliability and obtains the target reliability coefficient through a comprehensive quantitative assessment of reliability indicators, thereby improving the accuracy of the energy storage system reliability assessment results.

[0088] To achieve the above embodiments, such as Figure 2 As shown, this embodiment also provides a reliability assessment system 10 for energy storage systems, which includes: The acquisition module 201 periodically acquires the operating data of the energy storage system and the environmental characteristic data of the environment in which the energy storage system is located; The first data processing module 202 is used to extract reliability index data based on the operating data and obtain the reliability compliance coefficient based on the reliability index data. The second data processing module 203 is used to input environmental characteristic data into the target environmental factor interference assessment model to obtain the environmental interference coefficient; Optimization module 204 is used to optimize the reliability compliance coefficient based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system; Module 205 is used to determine whether the target reliability coefficient meets the reliability conditions. The alarm module 206 is used to generate alarm information and issue an alarm if the target reliability coefficient does not meet the reliability conditions.

[0089] In this embodiment of the disclosure, the first data processing module 202 is specifically used for: Extract reliability index data from the operational data; The corresponding reliability data is obtained by calculating based on the reliability index data and the corresponding preset index thresholds; The reliability data is weighted and summed to obtain the reliability compliance coefficient.

[0090] In one embodiment of the present invention, the first data processing module 202 is further configured to: The operating data of the energy storage system is divided into multiple candidate data, and multiple reliability index parameters are extracted from each candidate data. Each reliability index parameter is a parameter time series. Calculate the reliability of each reliability index parameter; Based on the reliability of each reliability index parameter, the reliability index data in the operational data is determined.

[0091] In one embodiment of the present invention, the first data processing module 202 is further configured to: Calculate the coupling reliability of each reliability index parameter; Calculate the numerical reliability of each reliability index parameter; Based on the coupling reliability and numerical reliability of each reliability index parameter, the parameter reliability of each reliability index parameter is determined.

[0092] In one embodiment of the present invention, the first data processing module 202 is further configured to: Calculate the correlation of changes between any two reliability index parameters in each set of candidate data; Calculate the coupling degree between any two reliability index parameters based on the correlation of changes; Based on the coupling degree, calculate the coupling reliability of each reliability index parameter.

[0093] In one embodiment of the present invention, the first data processing module 202 is further configured to: Calculate the stability of each reliability index parameter; Based on the stability of each reliability index parameter, calculate the numerical reliability of each reliability index parameter.

[0094] In one embodiment of the present invention, the optimization module 204 is specifically used for: Based on the environmental disturbance coefficient, the reliability compliance coefficient is optimized using an optimization formula to obtain the target reliability coefficient of the energy storage system. The optimization formula is as follows:

[0095] in, The target reliability coefficient, The reliability compliance coefficient, As a moderating factor of environmental impact, This represents the environmental interference coefficient.

[0096] In one embodiment of the present invention, the determining module 205 is specifically used for: If the target reliability coefficient is greater than the preset reliability value, then the reliability condition is determined to be met. If the target reliability coefficient is less than or equal to the preset reliability value, then the reliability condition is determined not to be met.

[0097] According to an embodiment of the present invention, the reliability assessment system for an energy storage system periodically acquires operational data of the energy storage system and environmental characteristic data of the environment in which the energy storage system is located; extracts reliability index data based on the operational data and obtains a reliability compliance coefficient based on the reliability index data; inputs the environmental characteristic data into a target environmental factor interference assessment model to obtain an environmental interference coefficient; optimizes the reliability compliance coefficient based on the environmental interference coefficient to obtain a target reliability coefficient for the energy storage system; determines whether the target reliability coefficient meets the reliability conditions; and if the target reliability coefficient does not meet the reliability conditions, generates an alarm message and issues an alarm. This invention obtains the environmental interference coefficient through environmental characteristic data of the environment in which the energy storage system is located, and uses the environmental interference coefficient to optimize the reliability compliance coefficient obtained from the operational data of the energy storage system to obtain the target reliability coefficient. It considers the impact of the environment on its reliability and obtains the target reliability coefficient through a comprehensive quantitative assessment of reliability indicators, thereby improving the accuracy of the reliability assessment results for the energy storage system.

[0098] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0099] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A reliability assessment method for an energy storage system, characterized in that, The method includes: Periodically acquire operational data of the energy storage system and environmental characteristic data of the environment in which the energy storage system is located; Reliability index data is extracted based on the operational data, and a reliability compliance coefficient is obtained based on the reliability index data. The environmental characteristic data is input into the target environmental factor interference assessment model to obtain the environmental interference coefficient; The reliability compliance coefficient is optimized based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system. Determine whether the target reliability coefficient meets the reliability conditions; if the target reliability coefficient does not meet the reliability conditions, generate alarm information and issue an alarm.

2. The method according to claim 1, characterized in that, The step of extracting reliability index data based on the operational data and obtaining a reliability compliance coefficient based on the reliability index data includes: Extract reliability index data from the operational data; The corresponding reliability data is obtained by calculating based on the reliability index data and the corresponding preset index threshold. The reliability data is weighted and summed to obtain the reliability compliance coefficient.

3. The method according to claim 2, characterized in that, The extraction of reliability index data from the operational data includes: The operating data of the energy storage system is divided into multiple candidate data, and multiple reliability index parameters are extracted from each candidate data, wherein each reliability index parameter is a parameter time series. Calculate the reliability of each of the aforementioned reliability index parameters; Based on the reliability of each of the aforementioned reliability index parameters, the reliability index data in the operational data is determined.

4. The method according to claim 3, characterized in that, The calculation of the reliability index parameters includes: Calculate the coupling reliability of each of the reliability index parameters; Calculate the numerical reliability of each of the reliability index parameters; Based on the coupling reliability and the numerical reliability of each reliability index parameter, the parameter reliability of each reliability index parameter is determined.

5. The method according to claim 4, characterized in that, The calculation of the coupling reliability of each of the reliability index parameters includes: Calculate the correlation of the amount of change between any two reliability index parameters in each set of candidate data; Based on the correlation of the changes, calculate the coupling degree between any two reliability index parameters; Based on the coupling degree, the coupling reliability of each reliability index parameter is calculated.

6. The method according to claim 4, characterized in that, The calculation of the numerical reliability of each of the reliability index parameters includes: Calculate the stability of each of the aforementioned reliability index parameters; The numerical reliability of each reliability index parameter is calculated based on the stability of each reliability index parameter.

7. The method according to claim 1, characterized in that, The step of optimizing the reliability compliance coefficient based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system includes: optimizing the reliability compliance coefficient based on the environmental interference coefficient using an optimization formula to obtain the target reliability coefficient of the energy storage system, wherein the optimization formula is: in, The target reliability coefficient, The reliability compliance coefficient, As a moderating factor of environmental impact, This represents the environmental interference coefficient.

8. The method according to claim 1, characterized in that, Determining whether the target reliability coefficient meets the reliability conditions includes: If the target reliability coefficient is greater than the preset reliability value, then the reliability condition is determined to be met. If the target reliability coefficient is less than or equal to the preset reliability value, then the reliability condition is determined not to be met.

9. A reliability assessment system for an energy storage system, characterized in that, The system includes: The acquisition module periodically acquires the operating data of the energy storage system and the environmental characteristic data of the environment in which the energy storage system is located; The first data processing module is used to extract reliability index data based on the operation data, and to obtain the reliability compliance coefficient based on the reliability index data. The second data processing module is used to input the environmental characteristic data into the target environmental factor interference assessment model to obtain the environmental interference coefficient. The optimization module is used to optimize the reliability compliance coefficient based on the environmental interference coefficient to obtain the target reliability coefficient of the energy storage system. The determination module is used to determine whether the target reliability coefficient meets the reliability conditions; An alarm module is used to generate alarm information and issue an alarm if the target reliability coefficient does not meet the reliability conditions.

10. A computer storage medium, wherein, The computer storage medium stores computer-executable instructions; when executed by a processor, the computer-executable instructions can implement the method as described in any one of claims 1-8.

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