Zone area distributed energy storage system operation and maintenance method, system and device based on edge-cloud collaboration and medium

Abstract

The invention provides a zone area distributed energy storage system operation and maintenance method, system and device based on edge-cloud collaboration and a medium, and the method comprises the steps: receiving the real-time operation data of a zone area single battery through an edge calculation node, carrying out the multi-stage edge diagnosis of the real-time operation data based on a dynamic threshold value, and determining a suspected problem single battery; performing problem checking on the suspected problem single battery through the cloud operation and maintenance platform, and determining the problem battery and the residual life thereof; and generating an operation and maintenance strategy based on the residual life and position of the problem battery, and carrying out optimization control on the distributed energy storage system. According to the invention, on the basis of an edge-cloud collaborative architecture, multi-condition rapid and accurate positioning of a defective battery is realized through multi-stage edge diagnosis and cloud problem checking, and the fault early warning false alarm rate is greatly reduced; and meanwhile, residual life prediction is performed on the defective battery, and an operation and maintenance strategy can be generated through the cloud operation and maintenance platform to reduce burden of a short-life transformer area, so that the operation and maintenance cost is reduced, and the operation and maintenance efficiency and economy are improved.

Description

Technical Field

[0001] This invention belongs to the field of power system energy storage technology, specifically relating to an operation and maintenance method, system, equipment and medium for a distributed energy storage system in a transformer substation based on edge-cloud collaboration. Background Technology

[0002] The continuous expansion of installed capacity of new energy sources such as wind power and photovoltaics poses a severe challenge to the stable operation of the power system due to their volatility and intermittency. Electrochemical energy storage, as a key support for building new power systems, has developed rapidly due to its high energy density and flexible deployment advantages. However, the large-scale application of electrochemical energy storage still faces three major bottlenecks:

[0003] 1. Battery life degradation issue – Battery cycle life is significantly affected by the depth of charge and discharge, with a capacity degradation of 20%-30% over the entire life cycle, which restricts economic efficiency;

[0004] 2. Significant safety hazards – the risk of thermal runaway and the flammability of the electrolyte lead to frequent safety accidents;

[0005] 3. Insufficient intelligent operation and maintenance - The traditional mode of relying on static threshold alarms and manual diagnosis is difficult to cope with complex operating conditions in the transformer area (such as a load peak-to-valley difference of 10:1 and drastic temperature fluctuations), resulting in delayed fault location (>4 hours), false alarm rate >25%, and system availability generally below 90%.

[0006] Given the practical need to improve fault early warning accuracy (≥95%) and operation and maintenance response speed (≤30 minutes), existing technologies are insufficient to meet the requirements. There is an urgent need to break through reliability bottlenecks through new technologies to provide core support for the safe and efficient operation of new power systems. Summary of the Invention

[0007] To overcome the shortcomings of the existing technology, this invention proposes an operation and maintenance method for a distributed energy storage system based on edge-cloud collaboration, comprising:

[0008] The system receives real-time operating data of individual cells in the distribution area through edge computing nodes, and performs multi-level edge diagnosis on the real-time operating data based on the calculated dynamic thresholds to diagnose suspected problematic individual cells in the distribution area.

[0009] The suspected problematic individual battery cells are checked through the cloud-based operation and maintenance platform to identify the problematic batteries in the transformer area and predict their remaining lifespan.

[0010] Based on the remaining lifespan and location of the problematic batteries, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the distributed energy storage system is optimized and controlled based on the operation and maintenance strategy.

[0011] Preferably, the step of performing multi-level edge diagnosis on the real-time operating data based on the calculated dynamic threshold to diagnose suspected problematic individual cells in the transformer substation includes:

[0012] Based on the real-time operating data, a dynamic adaptive threshold algorithm is used to compensate for multiple parameters of the reference internal resistance of the individual battery cells in the distribution area, and the dynamic threshold of the battery internal resistance is calculated; the real-time operating data includes real-time internal resistance, real-time voltage and real-time temperature;

[0013] The real-time internal resistance of the individual cells in the transformer area is compared with the dynamic threshold of the cell internal resistance to obtain the individual cell-level diagnostic results.

[0014] When the individual cell-level diagnostic result is abnormal, the real-time voltage of the individual cell in the distribution area is compared with the cluster voltage of the cluster to which the individual cell in the distribution area belongs to perform differential voltage diagnosis to obtain the cluster-level diagnostic result.

[0015] When the cluster-level diagnostic result is abnormal, the real-time temperature of the individual cell in the distribution area and the stack temperature of the stack where the individual cell in the distribution area is located are compared to obtain the stack-level diagnostic result.

[0016] When the stack-level diagnostic result is abnormal, the individual cell in the distribution area is identified as a suspected problematic individual cell.

[0017] Preferably, the dynamic threshold of the battery internal resistance is expressed as:

[0018]

[0019] Among them, R t R is the dynamic threshold of the battery's internal resistance. b The reference internal resistance is α, the temperature compensation coefficient is α, and T is the real-time temperature of the individual cell in the transformer area. r For reference temperature, α(TT) r ) represents the temperature parameter compensation term, β represents the current fluctuation coefficient, and I r I is the root mean square current of the individual cells in the transformer area. n The rated current of the individual battery cells in the aforementioned distribution area. This is a compensation term for current fluctuation parameters.

[0020] Preferably, the step of verifying the suspected problematic individual battery cells through a cloud-based operation and maintenance platform, and identifying the problematic batteries among the individual battery cells in the distribution area, includes:

[0021] The cloud-based operation and maintenance platform receives ambient temperature monitoring data from the transformer area's ambient temperature monitoring device for the suspected problematic individual battery cells.

[0022] When the ambient temperature monitoring data is greater than the set ambient temperature threshold, the suspected problematic single cell battery is determined to be a problematic battery.

[0023] Preferably, the prediction of the remaining lifespan of the problematic battery includes:

[0024] Based on the real-time operating data of the problematic battery, a weighted linear model is used to calculate the ratio of the designed life of the problematic battery to multiple weighted summation battery aging factors, thereby obtaining the remaining life of the problematic battery.

[0025] Preferably, the remaining lifespan of the problematic battery is expressed as:

[0026]

[0027] Where RUL represents the remaining lifespan of the problematic battery; L0 represents the design lifespan of the problematic battery; and T represents the real-time temperature of the problematic battery, ∫(T-25). 2 dt is the temperature aging factor; ΔR is the increase in internal resistance per unit; Δt is the time corresponding to the increase in internal resistance per unit. is the internal resistance degradation factor; EFC is the equivalent number of full cycles at different discharge depths, i.e., the cycle loss factor; a1, a2, and a3 are the weights of the temperature aging factor, internal resistance degradation factor, and cycle loss factor, respectively, obtained through accelerated aging experiments.

[0028] Preferably, the step of generating an operation and maintenance strategy based on the remaining lifespan and location of the problematic battery through the cloud-based operation and maintenance platform, and optimizing and controlling the distributed energy storage system based on the operation and maintenance strategy, includes:

[0029] Based on the remaining lifespan of the problematic batteries, the shortest remaining lifespan of all problematic batteries in the distribution area is taken as the operating lifespan of the distributed energy storage system in the distribution area.

[0030] Based on the operational lifespan of the distributed energy storage system in the transformer area and the location of all problematic batteries within the transformer area, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the operation and maintenance strategy is distributed to the edge computing nodes. The distributed energy storage system is then optimized and controlled based on the operation and maintenance strategy.

[0031] Based on the same inventive concept, this invention also provides an operation and maintenance system for a distributed energy storage system in a transformer substation based on edge-cloud collaboration, comprising:

[0032] The primary diagnostic module is used to receive real-time operating data of individual batteries in the distribution area through edge computing nodes, and perform multi-level edge diagnostics on the real-time operating data based on the calculated dynamic thresholds to diagnose suspected problematic individual batteries in the distribution area.

[0033] The problem verification module is used to verify the suspected problematic individual battery cells through the cloud-based operation and maintenance platform, identify the problematic batteries in the individual battery cells of the transformer area, and predict the remaining life of the problematic batteries.

[0034] The strategy generation module is used to generate operation and maintenance strategies through the cloud operation and maintenance platform based on the remaining lifespan and location of the problematic battery, and to optimize and control the distributed energy storage system based on the operation and maintenance strategies.

[0035] Preferably, the primary diagnostic module is specifically used for:

[0036] Based on the real-time operating data, a dynamic adaptive threshold algorithm is used to compensate for multiple parameters of the reference internal resistance of the individual battery cells in the distribution area, and the dynamic threshold of the battery internal resistance is calculated; the real-time operating data includes real-time internal resistance, real-time voltage and real-time temperature;

[0037] The real-time internal resistance of the individual cells in the transformer area is compared with the dynamic threshold of the cell internal resistance to obtain the individual cell-level diagnostic results.

[0038] When the individual cell-level diagnostic result is abnormal, the real-time voltage of the individual cell in the distribution area is compared with the cluster voltage of the cluster to which the individual cell in the distribution area belongs to perform differential voltage diagnosis to obtain the cluster-level diagnostic result.

[0039] When the cluster-level diagnostic result is abnormal, the real-time temperature of the individual cell in the distribution area and the stack temperature of the stack where the individual cell in the distribution area is located are compared to obtain the stack-level diagnostic result.

[0040] When the stack-level diagnostic result is abnormal, the individual cell in the distribution area is identified as a suspected problematic individual cell.

[0041] Preferably, the dynamic threshold of the battery internal resistance is expressed as:

[0042]

[0043] Among them, R t R is the dynamic threshold of the battery's internal resistance. b The reference internal resistance is α, the temperature compensation coefficient is α, and T is the real-time temperature of the individual cell in the transformer area. r For reference temperature, α(TT) r ) represents the temperature parameter compensation term, β represents the current fluctuation coefficient, and I r I is the root mean square current of the individual cells in the transformer area. n The rated current of the individual battery cells in the aforementioned distribution area. This is a compensation term for current fluctuation parameters.

[0044] Preferably, the problem verification module is specifically used for:

[0045] The cloud-based operation and maintenance platform receives ambient temperature monitoring data from the transformer area's ambient temperature monitoring device for the suspected problematic individual battery cells.

[0046] When the ambient temperature monitoring data is greater than the set ambient temperature threshold, the suspected problematic single cell battery is determined to be a problematic battery.

[0047] Preferably, the problem verification module is specifically used for:

[0048] Based on the real-time operating data of the problematic battery, a weighted linear model is used to calculate the ratio of the designed life of the problematic battery to multiple weighted summation battery aging factors, thereby obtaining the remaining life of the problematic battery.

[0049] Preferably, the remaining lifespan of the problematic battery is expressed as:

[0050]

[0051] Where RUL represents the remaining lifespan of the problematic battery; L0 represents the design lifespan of the problematic battery; and T represents the real-time temperature of the problematic battery, ∫(T-25). 2 dt is the temperature aging factor; ΔR is the increase in internal resistance per unit; Δt is the time corresponding to the increase in internal resistance per unit. is the internal resistance degradation factor; EFC is the equivalent number of full cycles at different discharge depths, i.e., the cycle loss factor; a1, a2, and a3 are the weights of the temperature aging factor, internal resistance degradation factor, and cycle loss factor, respectively, obtained through accelerated aging experiments.

[0052] Preferably, the strategy generation module is specifically used for:

[0053] Based on the remaining lifespan of the problematic batteries, the shortest remaining lifespan of all problematic batteries in the distribution area is taken as the operating lifespan of the distributed energy storage system in the distribution area.

[0054] Based on the operational lifespan of the distributed energy storage system in the transformer area and the location of all problematic batteries within the transformer area, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the operation and maintenance strategy is distributed to the edge computing nodes. The distributed energy storage system is then optimized and controlled based on the operation and maintenance strategy.

[0055] Based on the same inventive concept, the present invention also provides an operation and maintenance system for a distributed energy storage system in a transformer substation based on edge-cloud collaboration, comprising: a transformer substation energy storage information acquisition terminal, an edge computing node, and a cloud-based operation and maintenance platform that are connected in sequence via communication.

[0056] The energy storage information acquisition terminal for the distribution area is used to collect real-time operating data of individual batteries in the distribution area.

[0057] Edge computing nodes are used to receive real-time operating data of individual cells in the distribution area, and perform multi-level edge diagnosis on the real-time operating data based on the calculated dynamic threshold to diagnose suspected problematic individual cells in the distribution area.

[0058] The cloud-based operation and maintenance platform is used to verify the suspected problematic individual battery cells, identify the problematic batteries in the distribution area, and predict the remaining lifespan of the problematic batteries. Based on the remaining lifespan and location of the problematic batteries, an operation and maintenance strategy is generated, and the distributed energy storage system is optimized and controlled based on the operation and maintenance strategy.

[0059] Preferably, the edge computing node is specifically used for:

[0060] Based on the real-time operating data, a dynamic adaptive threshold algorithm is used to compensate for multiple parameters of the reference internal resistance of the individual battery cells in the distribution area, and the dynamic threshold of the battery internal resistance is calculated; the real-time operating data includes real-time internal resistance, real-time voltage and real-time temperature;

[0061] The real-time internal resistance of the individual cells in the transformer area is compared with the dynamic threshold of the cell internal resistance to obtain the individual cell-level diagnostic results.

[0062] When the individual cell-level diagnostic result is abnormal, the real-time voltage of the individual cell in the distribution area is compared with the cluster voltage of the cluster to which the individual cell in the distribution area belongs to perform differential voltage diagnosis to obtain the cluster-level diagnostic result.

[0063] When the cluster-level diagnostic result is abnormal, the real-time temperature of the individual cell in the distribution area and the stack temperature of the stack where the individual cell in the distribution area is located are compared to obtain the stack-level diagnostic result.

[0064] When the stack-level diagnostic result is abnormal, the individual cell in the distribution area is identified as a suspected problematic individual cell.

[0065] Preferably, the dynamic threshold of the battery internal resistance is expressed as:

[0066]

[0067] Among them, R t R is the dynamic threshold of the battery's internal resistance. b The reference internal resistance is α, the temperature compensation coefficient is α, and T is the real-time temperature of the individual cell in the transformer area. r For reference temperature, α(TT) r ) represents the temperature parameter compensation term, β represents the current fluctuation coefficient, and I r I is the root mean square current of the individual cells in the transformer area. n The rated current of the individual battery cells in the aforementioned distribution area. This is a compensation term for current fluctuation parameters.

[0068] Preferably, the cloud-based operation and maintenance platform includes a first cloud module, used for:

[0069] Receive ambient temperature monitoring data of the suspected problematic individual battery cells from the environmental temperature monitoring device in the receiving area;

[0070] When the ambient temperature monitoring data is greater than the set ambient temperature threshold, the suspected problematic single cell battery is determined to be a problematic battery.

[0071] Preferably, the cloud-based operation and maintenance platform includes a second cloud module, used for:

[0072] Based on the real-time operating data of the problematic battery, a weighted linear model is used to calculate the ratio of the designed life of the problematic battery to multiple weighted summation battery aging factors, thereby obtaining the remaining life of the problematic battery.

[0073] Preferably, the remaining lifespan of the problematic battery is expressed as:

[0074]

[0075] Where RUL represents the remaining lifespan of the problematic battery; L0 represents the design lifespan of the problematic battery; and T represents the real-time temperature of the problematic battery, ∫(T-25). 2 dt is the temperature aging factor; ΔR is the increase in internal resistance per unit; Δt is the time corresponding to the increase in internal resistance per unit. is the internal resistance degradation factor; EFC is the equivalent number of full cycles at different discharge depths, i.e., the cycle loss factor; a1, a2, and a3 are the weights of the temperature aging factor, internal resistance degradation factor, and cycle loss factor, respectively, obtained through accelerated aging experiments.

[0076] Preferably, the cloud-based operation and maintenance platform includes a third cloud module, used for:

[0077] Based on the remaining lifespan of the problematic batteries, the shortest remaining lifespan of all problematic batteries in the distribution area is taken as the operating lifespan of the distributed energy storage system in the distribution area.

[0078] Based on the operational lifespan of the distributed energy storage system in the transformer area and the location of all problematic batteries within the transformer area, an operation and maintenance strategy is generated and distributed to the edge computing nodes. The distributed energy storage system is then optimized and controlled based on the operation and maintenance strategy.

[0079] Based on the same inventive concept, the present invention also provides a computer device, comprising: one or more processors;

[0080] Memory, used to store one or more programs;

[0081] When the one or more programs are executed by the one or more processors, the operation and maintenance method of the distributed energy storage system based on edge-cloud collaboration, as described above, is implemented.

[0082] Based on the same inventive concept, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the operation and maintenance method of a distributed energy storage system based on edge-cloud collaboration as described above.

[0083] Compared with the closest existing technology, the present invention has the following beneficial effects:

[0084] This invention provides an operation and maintenance method, system, device, and medium for a distributed energy storage system based on edge-cloud collaboration. The method includes: receiving real-time operating data of individual batteries in the distribution area through an edge computing node; performing multi-level edge diagnosis on the real-time operating data based on a calculated dynamic threshold to diagnose suspected problematic individual batteries in the distribution area; verifying the suspected problematic individual batteries through a cloud-based operation and maintenance platform to identify problematic batteries in the distribution area and predicting the remaining lifespan of the problematic batteries; and generating an operation and maintenance strategy based on the remaining lifespan and location of the problematic batteries through the cloud-based operation and maintenance platform to optimize and control the distributed energy storage system. This invention is based on an edge-cloud collaborative architecture. Through multi-level edge diagnostics and problem verification by the cloud-based operation and maintenance platform, it achieves rapid and accurate location of faulty batteries under multiple conditions, significantly reducing the false alarm rate of fault warnings. At the same time, the cloud-based operation and maintenance platform predicts the remaining lifespan of faulty batteries. Based on the remaining lifespan and location of the faulty batteries, the cloud-based operation and maintenance platform can generate operation and maintenance strategies to reduce the burden on short-lifespan areas and provide a basis for other means to improve the operating lifespan of the areas, thereby reducing operation and maintenance costs and improving operation and maintenance efficiency and economy. Attached Figure Description

[0085] Figure 1 A schematic diagram of the operation and maintenance method of a distributed energy storage system in a transformer substation based on edge-cloud collaboration provided by the present invention;

[0086] Figure 2 This invention provides a schematic diagram of the problem battery location process.

[0087] Figure 3 A schematic diagram of the operation and maintenance system architecture of the distributed energy storage system in the substation area based on edge-cloud collaboration provided by the present invention;

[0088] Figure 4 A schematic diagram of the operation and maintenance system structure of a distributed energy storage system in a transformer substation based on edge-cloud collaboration is provided for this invention.

[0089] Figure 5 This is a schematic diagram of an electronic device structure provided by the present invention. Detailed Implementation

[0090] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0091] Example 1:

[0092] This invention provides an operation and maintenance method for a distributed energy storage system in a transformer substation based on edge-cloud collaboration, such as... Figure 1 As shown, it includes:

[0093] S1. Receive real-time operating data of individual batteries in the distribution area through edge computing nodes, and perform multi-level edge diagnosis on the real-time operating data based on the calculated dynamic threshold to diagnose suspected problematic individual batteries in the distribution area.

[0094] S2. The suspected problematic individual battery is checked through the cloud operation and maintenance platform to identify the problematic battery in the transformer area and predict the remaining life of the problematic battery.

[0095] S3. Based on the remaining lifespan and location of the problematic battery, generate an operation and maintenance strategy through the cloud-based operation and maintenance platform, and optimize and control the distributed energy storage system based on the operation and maintenance strategy.

[0096] Considering that existing technologies suffer from poor dynamic adaptability and lack of multi-source collaboration, making them ill-suited to new demands, this invention, based on an edge-cloud collaborative architecture, achieves rapid and accurate location of problematic batteries under multiple conditions through multi-level edge diagnostics and cloud-based operation and maintenance platform problem verification, significantly reducing the false alarm rate of fault warnings. Simultaneously, the cloud-based operation and maintenance platform predicts the remaining lifespan of problematic batteries. Based on the remaining lifespan and location of the problematic batteries, the cloud-based operation and maintenance platform can generate operation and maintenance strategies to alleviate the burden on short-life distribution areas, providing a basis for other methods to improve the operational lifespan of these areas. This achieves a closed-loop system of real-time edge perception → intelligent cloud decision-making → precise strategy execution, addressing the core pain points of distributed energy storage in distribution areas across three dimensions: adaptability to extreme operating conditions, fault location efficiency, and full-cycle economics. This reduces operation and maintenance costs, improves operation and maintenance efficiency, economy, and energy storage reliability, providing a technological foundation for the safe operation of new power systems.

[0097] A single cell in a distribution network serves as the smallest electrochemical unit in a distributed energy storage system. Multiple cells are connected in series and parallel to form independent battery clusters, and multiple clusters are connected in parallel to form a battery stack. A single cell in a distribution network is deemed problematic in the following situations: increased internal resistance leading to severe performance degradation, and parameters such as temperature and voltage exceeding safety limits. In S1 above, such as... Figure 2As shown, the multi-level edge diagnosis includes three levels: single cell level, cluster level, and stack level. Through the three-level edge diagnosis architecture, parameters such as internal resistance, temperature, and voltage of single cells are diagnosed step by step from single cell to cluster to stack. By making comprehensive judgments, the problem cells can be located quickly and accurately under multiple conditions, which can significantly reduce the false alarm rate of fault warning.

[0098] In this embodiment, the process of diagnosing suspected problematic individual cells in S1 above may include:

[0099] S101. Based on the real-time operating data, a dynamic adaptive threshold algorithm is used to compensate for multiple parameters of the reference internal resistance of the individual battery in the distribution area, and the dynamic threshold of the battery internal resistance is calculated; the real-time operating data includes real-time internal resistance, real-time voltage and real-time temperature;

[0100] S102. Compare and diagnose the real-time internal resistance of the individual cell in the distribution area with the dynamic threshold of the battery internal resistance to obtain the individual cell-level diagnostic result.

[0101] S103. When the single-cell level diagnostic result is abnormal, the real-time voltage of the single cell in the distribution area is compared with the cluster voltage of the cluster in which the single cell in the distribution area is located to perform differential voltage diagnosis and obtain the cluster level diagnostic result.

[0102] S104. When the cluster-level diagnostic result is abnormal, perform temperature difference diagnosis between the real-time temperature of the individual cell in the distribution area and the stack temperature of the stack where the individual cell in the distribution area is located to obtain the stack-level diagnostic result.

[0103] S105. When the stack-level diagnostic result is abnormal, the individual cell in the distribution area is determined to be a suspected problematic individual cell.

[0104] Specifically, to improve the performance of traditional thresholds in complex operating environments (high temperature, low temperature, etc.) and address issues such as failure and false alarms, a condition compensation factor is introduced to correct the internal resistance threshold when calculating the dynamic threshold of battery internal resistance in the aforementioned S101 edge computing node, thereby enhancing adaptability to extreme operating conditions. The condition compensation factor includes temperature parameter compensation terms and current fluctuation parameter compensation terms.

[0105] In this embodiment, when calculating the dynamic threshold of the battery internal resistance, the real-time operating data also includes the real-time current of the individual battery cells in the distribution area.

[0106] In this embodiment, the real-time operating data is collected through a power grid energy storage information acquisition terminal connected to a single battery cell in the power grid area, such as... Figure 3 As shown, the number of energy storage information acquisition terminals in the distribution area, from 1 to n, matches the number of individual batteries in a battery stack. One battery stack corresponds to one edge computing node. The n battery stacks in the distributed energy storage system are connected to the cloud operation and maintenance platform via n edge computing nodes.

[0107] In this embodiment, the dynamic threshold of battery internal resistance is expressed as:

[0108]

[0109] Among them, R t R is the dynamic threshold of the battery's internal resistance. b α is the reference internal resistance (e.g., nominal internal resistance at 25℃), α is the temperature compensation coefficient (e.g., 0.005 / ℃ for lithium iron phosphate), and T is the real-time temperature of the individual cells in the transformer area. r For the reference temperature (usually 25℃), α(TT) r ) represents the temperature parameter compensation term, β represents the current fluctuation coefficient (measured and calibrated), and I r I is the root mean square current of the individual cells in the transformer area. n The rated current of the individual battery cells in the aforementioned distribution area. This is a compensation term for current fluctuation parameters.

[0110] It should be noted that the root mean square current of the individual battery in the transformer area is the root mean square current over 2 hours, which is calculated based on the root mean square current calculation formula using the real-time current over 2 hours.

[0111] When performing single-cell level diagnosis in S102 above: if the real-time internal resistance of a single cell in the distribution area is greater than the dynamic threshold of the cell's internal resistance, it is marked as a suspected problem cell and enters the secondary judgment condition, that is, enters the cluster level diagnosis.

[0112] Specifically, a single-item-level diagnosis is represented as: R d >R t ;

[0113] Among them, R d R represents the real-time internal resistance of a single cell. t This represents the battery's threshold internal resistance.

[0114] In the above S103, when performing cluster-level diagnosis, i.e. cluster-level voltage equalization analysis: the real-time voltage of the individual cell in the power distribution area is compared with the cluster voltage of the cluster in which the individual cell in the power distribution area is located. If the difference overflows, i.e. the difference between the real-time voltage of the individual cell in the power distribution area and the highest and / or lowest individual cell voltage of the cluster in which the individual cell in the power distribution area is located is greater than the set difference threshold, the third-level judgment condition is entered, i.e., the stack-level diagnosis is entered.

[0115] Specifically, differential pressure diagnosis, i.e., inter-cluster differential pressure comparison, is expressed as:

[0116]

[0117] Among them, V max This represents the highest single-cell voltage in the cluster; Vmin V is the lowest single-cell voltage in this cluster. t This indicates the real-time voltage of the suspected problematic individual battery cell.

[0118] Temperature acquisition and temperature difference judgment of individual cells in the stack should be performed on the edge side. Therefore, when performing stack-level temperature difference diagnosis in S104 above: based on stack-level temperature field reconstruction and hot spot location, the highest and lowest individual cell temperatures in the stack are determined. If the temperature difference exceeds the set temperature difference value, that is, the difference between the real-time temperature of the suspected problem cell and the highest and / or lowest individual cell temperatures in the stack where the cell in the stack is located is greater than the set temperature difference threshold, the cell in the stack is determined to be a suspected problem cell. The information is then pushed in combination with the edge stack coordinate information and abnormal information, and then the target cell is replaced to perform a new diagnosis process.

[0119] Specifically, temperature difference diagnosis is expressed as follows:

[0120] Among them, T max T represents the highest temperature of a single cell in the stack. min T is the lowest temperature of a single cell in the stack. t This is the real-time temperature of a suspected problematic individual battery cell; the standard requires immediate action if the temperature difference is greater than 10 degrees Celsius. Based on engineering experience, the temperature difference value for lithium iron phosphate batteries is generally set at 7 degrees Celsius (and for liquid-cooled batteries, it is generally set at 4 degrees Celsius).

[0121] If an abnormal diagnosis is obtained after the edge computing node performs a three-level diagnosis, the result is sent to the cloud operation and maintenance platform. The cloud operation and maintenance platform performs full-domain data aggregation, complex model calculation, and multi-objective collaborative decision-making, upgrading the "sensory nerves" of the edge computing node layer to a "smart brain" to achieve global optimal control of the energy storage system's safety, economy, and lifespan.

[0122] In this embodiment, when a problematic battery is identified in step S2 above, the following may be included:

[0123] The cloud-based operation and maintenance platform receives ambient temperature monitoring data from the transformer area's ambient temperature monitoring device for the suspected problematic individual battery cells.

[0124] When the ambient temperature monitoring data is greater than the set ambient temperature threshold, the suspected problematic single cell battery is determined to be a problematic battery.

[0125] Specifically, the environmental temperature monitoring device in the transformer area uses devices such as infrared monitoring in the transformer area to automatically locate abnormal individual batteries on the cloud-based operation and maintenance platform, collect and compare their temperatures, and finally determine the problematic battery.

[0126] In addition, a cloud-based lifespan prediction model is proposed to predict the remaining lifespan of each transformer area, thereby reducing the burden on short-life transformer areas and providing a basis for other means to improve the operating lifespan of the transformer area.

[0127] In this embodiment, predicting the remaining lifespan of the problematic battery includes:

[0128] Based on the real-time operating data of the problematic battery, a weighted linear model is used to calculate the ratio of the designed life of the problematic battery to multiple weighted summation battery aging factors, thereby obtaining the remaining life of the problematic battery.

[0129] In this embodiment, the remaining lifespan of the problematic battery is expressed as:

[0130]

[0131] Where RUL represents the remaining lifespan of the problematic battery; L0 represents the design lifespan of the problematic battery (e.g., the initial lifespan of lithium iron phosphate is 8 years); and T represents the real-time temperature of the problematic battery, ∫(T-25). 2 dt is the temperature aging factor; ΔR is the increase in internal resistance per unit; Δt is the time corresponding to the increase in internal resistance per unit. is the internal resistance degradation factor; EFC is the equivalent number of full cycles at different discharge depths, i.e., the cycle loss factor; a1, a2, and a3 are the weights of the temperature aging factor, internal resistance degradation factor, and cycle loss factor, respectively, obtained by fitting through accelerated aging experiments; for example, a1 = 0.002, a2 ​​= 1.5, and a3 = 0.8 can also be obtained by empirical methods.

[0132] Among them, the equivalent total number of cycles DOD k Depth of discharge (DOD) refers to the percentage of total capacity that the battery releases during the k-th cycle (e.g., 100% DOD). k (Indicates complete discharge); Ck represents the damage coefficient per cycle, and n is the total number of actual cycles.

[0133] Taking phosphoric acid batteries as an example, their quantitative relationship is expressed as follows:

[0134]

[0135] Example: A battery undergoes 300 cycles, and the DOD distribution is as follows:

[0136] (1) 100 discharges at 30% depth (damage factor = 0.3): equivalent number of 100% DODs = 100 × 30 / 100 × 0.3 = 9;

[0137] (2) 200 discharges at 60% depth (damage factor = 0.7): equivalent 100% DOD counts = 200 × 60 / 100 × 0.7 = 84;

[0138] Total EFC = 9 + 84 = 93 cycles, and the remaining lifetime is: initial lifetime (2000 cycles) - 93 = 1907 cycles.

[0139] In this embodiment, the step of generating an operation and maintenance strategy based on the remaining lifespan and location of the problematic battery through the cloud-based operation and maintenance platform, and optimizing and controlling the distributed energy storage system based on the operation and maintenance strategy, includes:

[0140] Based on the remaining lifespan of the problematic batteries, the shortest remaining lifespan of all problematic batteries in the distribution area is taken as the operating lifespan of the distributed energy storage system in the distribution area.

[0141] Based on the operational lifespan of the distributed energy storage system in the transformer area and the location of all problematic batteries within the transformer area, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the operation and maintenance strategy is distributed to the edge computing nodes. The distributed energy storage system is then optimized and controlled based on the operation and maintenance strategy.

[0142] Specifically, such as Figure 3 As shown, after the cloud-based operation and maintenance platform distributes the operation and maintenance strategy to the edge computing node, the edge computing node transmits the received operation and maintenance strategy as a control command to the energy storage information collection terminal in the distribution area for on-site deployment.

[0143] The cloud-based operation and maintenance platform can generate the following operation and maintenance methods based on the prediction of the energy storage lifespan of the distribution area and the location of the weak / problematic batteries:

[0144] Method 1: Accurately identify problematic batteries and generate maintenance plan work orders. There are two types of maintenance work orders: emergency maintenance work orders and planned maintenance work orders.

[0145] Method 2: Based on the lifespan of the problematic batteries in the distribution area, the cloud-based operation and maintenance platform can instruct the cluster where the problematic battery is located to operate in a current balancing mode. The cluster where the problematic battery is located will operate with a low current, and the weight allocation and superposition of the current operating current will be distributed to the other clusters without problems while keeping the total operating power unchanged.

[0146] Method 3: When there are issues with the service life of a transformer substation, adjustments can be made by combining the operation of other transformer substations on the same feeder line. The operation method is the same as in Method 2.

[0147] Based on the above operation and maintenance method, the present invention can reduce operation and maintenance costs, generate a replacement priority list, and improve operation and maintenance efficiency and economy by relying on the edge-cloud collaborative architecture.

[0148] In summary, this invention, based on an edge-cloud collaborative architecture, proposes a dynamic threshold theory for internal resistance to achieve second-level anomaly diagnosis for distributed energy storage in power distribution areas, significantly reducing the false alarm rate of fault warnings. It also constructs a cloud-based lifetime prediction model to achieve global optimization of safety, lifetime, and economy. Overall, edge-cloud collaborative management improves system availability and reduces operation and maintenance costs.

[0149] Example 2:

[0150] Based on the same inventive concept, this invention also provides an operation and maintenance system for a distributed energy storage system in a transformer substation based on edge-cloud collaboration, such as... Figure 4 As shown, it includes:

[0151] The primary diagnostic module is used to receive real-time operating data of individual batteries in the distribution area through edge computing nodes, and perform multi-level edge diagnostics on the real-time operating data based on the calculated dynamic thresholds to diagnose suspected problematic individual batteries in the distribution area.

[0152] The problem verification module is used to verify the suspected problematic individual battery cells through the cloud-based operation and maintenance platform, identify the problematic batteries in the individual battery cells of the transformer area, and predict the remaining life of the problematic batteries.

[0153] The strategy generation module is used to generate operation and maintenance strategies through the cloud operation and maintenance platform based on the remaining lifespan and location of the problematic battery, and to optimize and control the distributed energy storage system based on the operation and maintenance strategies.

[0154] In this embodiment, the primary diagnostic module is specifically used for:

[0155] Based on the real-time operating data, a dynamic adaptive threshold algorithm is used to compensate for multiple parameters of the reference internal resistance of the individual battery cells in the distribution area, and the dynamic threshold of the battery internal resistance is calculated; the real-time operating data includes real-time internal resistance, real-time voltage and real-time temperature;

[0156] The real-time internal resistance of the individual cells in the transformer area is compared with the dynamic threshold of the cell internal resistance to obtain the individual cell-level diagnostic results.

[0157] When the individual cell-level diagnostic result is abnormal, the real-time voltage of the individual cell in the distribution area is compared with the cluster voltage of the cluster to which the individual cell in the distribution area belongs to perform differential voltage diagnosis to obtain the cluster-level diagnostic result.

[0158] When the cluster-level diagnostic result is abnormal, the real-time temperature of the individual cell in the distribution area and the stack temperature of the stack where the individual cell in the distribution area is located are compared to obtain the stack-level diagnostic result.

[0159] When the stack-level diagnostic result is abnormal, the individual cell in the distribution area is identified as a suspected problematic individual cell.

[0160] In this embodiment, the dynamic threshold of battery internal resistance is expressed as:

[0161]

[0162] Among them, R t R is the dynamic threshold of the battery's internal resistance. b The reference internal resistance is α, the temperature compensation coefficient is α, and T is the real-time temperature of the individual cell in the transformer area. r For reference temperature, α(TT) r ) represents the temperature parameter compensation term, β represents the current fluctuation coefficient, and I r I is the root mean square current of the individual cells in the transformer area. n The rated current of the individual battery cells in the aforementioned distribution area. This is a compensation term for current fluctuation parameters.

[0163] In this embodiment, the problem verification module is specifically used for:

[0164] The cloud-based operation and maintenance platform receives ambient temperature monitoring data from the transformer area's ambient temperature monitoring device for the suspected problematic individual battery cells.

[0165] When the ambient temperature monitoring data is greater than the set ambient temperature threshold, the suspected problematic single cell battery is determined to be a problematic battery.

[0166] In this embodiment, the problem verification module is specifically used for:

[0167] Based on the real-time operating data of the problematic battery, a weighted linear model is used to calculate the ratio of the designed life of the problematic battery to multiple weighted summation battery aging factors, thereby obtaining the remaining life of the problematic battery.

[0168] In this embodiment, the remaining lifespan of the problematic battery is expressed as:

[0169]

[0170] Where RUL represents the remaining lifespan of the problematic battery; L0 represents the design lifespan of the problematic battery; and T represents the real-time temperature of the problematic battery, ∫(T-25). 2 dt is the temperature aging factor; ΔR is the increase in internal resistance per unit; Δt is the time corresponding to the increase in internal resistance per unit. is the internal resistance degradation factor; EFC is the equivalent number of full cycles at different discharge depths, i.e., the cycle loss factor; a1, a2, and a3 are the weights of the temperature aging factor, internal resistance degradation factor, and cycle loss factor, respectively, obtained through accelerated aging experiments.

[0171] In this embodiment, the strategy generation module is specifically used for:

[0172] Based on the remaining lifespan of the problematic batteries, the shortest remaining lifespan of all problematic batteries in the distribution area is taken as the operating lifespan of the distributed energy storage system in the distribution area.

[0173] Based on the operational lifespan of the distributed energy storage system in the transformer area and the location of all problematic batteries within the transformer area, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the operation and maintenance strategy is distributed to the edge computing nodes. The distributed energy storage system is then optimized and controlled based on the operation and maintenance strategy.

[0174] Example 3:

[0175] Based on the same inventive concept, this invention also provides an operation and maintenance system for a distributed energy storage system in a transformer substation based on edge-cloud collaboration, such as... Figure 3 As shown, it includes: a distribution area energy storage information acquisition terminal, an edge computing node, and a cloud operation and maintenance platform that are connected in sequence via communication;

[0176] The energy storage information acquisition terminal for the distribution area is used to collect real-time operating data of individual batteries in the distribution area.

[0177] Edge computing nodes are used to receive real-time operating data of individual cells in the distribution area, and perform multi-level edge diagnosis on the real-time operating data based on the calculated dynamic threshold to diagnose suspected problematic individual cells in the distribution area.

[0178] The cloud-based operation and maintenance platform is used to verify the suspected problematic individual battery cells, identify the problematic batteries in the distribution area, and predict the remaining lifespan of the problematic batteries. Based on the remaining lifespan and location of the problematic batteries, an operation and maintenance strategy is generated, and the distributed energy storage system is optimized and controlled based on the operation and maintenance strategy.

[0179] In this embodiment, the edge computing node is specifically used for:

[0180] Based on the real-time operating data, a dynamic adaptive threshold algorithm is used to compensate for multiple parameters of the reference internal resistance of the individual battery cells in the distribution area, and the dynamic threshold of the battery internal resistance is calculated; the real-time operating data includes real-time internal resistance, real-time voltage and real-time temperature;

[0181] The real-time internal resistance of the individual cells in the transformer area is compared with the dynamic threshold of the cell internal resistance to obtain the individual cell-level diagnostic results.

[0182] When the individual cell-level diagnostic result is abnormal, the real-time voltage of the individual cell in the distribution area is compared with the cluster voltage of the cluster to which the individual cell in the distribution area belongs to perform differential voltage diagnosis to obtain the cluster-level diagnostic result.

[0183] When the cluster-level diagnostic result is abnormal, the real-time temperature of the individual cell in the distribution area and the stack temperature of the stack where the individual cell in the distribution area is located are compared to obtain the stack-level diagnostic result.

[0184] When the stack-level diagnostic result is abnormal, the individual cell in the distribution area is identified as a suspected problematic individual cell.

[0185] In this embodiment, the dynamic threshold of battery internal resistance is expressed as:

[0186]

[0187] Among them, R t R is the dynamic threshold of the battery's internal resistance. b The reference internal resistance is α, the temperature compensation coefficient is α, and T is the real-time temperature of the individual cell in the transformer area. r For reference temperature, α(TT) r ) represents the temperature parameter compensation term, β represents the current fluctuation coefficient, and I r I is the root mean square current of the individual cells in the transformer area. n The rated current of the individual battery cells in the aforementioned distribution area. This is a compensation term for current fluctuation parameters.

[0188] In this embodiment, the cloud-based operation and maintenance platform includes a first cloud module, used for:

[0189] Receive ambient temperature monitoring data of the suspected problematic individual battery cells from the environmental temperature monitoring device in the receiving area;

[0190] When the ambient temperature monitoring data is greater than the set ambient temperature threshold, the suspected problematic single cell battery is determined to be a problematic battery.

[0191] In this embodiment, the cloud-based operation and maintenance platform includes a second cloud module, used for:

[0192] Based on the real-time operating data of the problematic battery, a weighted linear model is used to calculate the ratio of the designed life of the problematic battery to multiple weighted summation battery aging factors, thereby obtaining the remaining life of the problematic battery.

[0193] In this embodiment, the remaining lifespan of the problematic battery is expressed as:

[0194]

[0195] Where RUL represents the remaining lifespan of the problematic battery; L0 represents the design lifespan of the problematic battery; and T represents the real-time temperature of the problematic battery, ∫(T-25). 2 dt is the temperature aging factor; ΔR is the increase in internal resistance per unit; Δt is the time corresponding to the increase in internal resistance per unit. is the internal resistance degradation factor; EFC is the equivalent number of full cycles at different discharge depths, i.e., the cycle loss factor; a1, a2, and a3 are the weights of the temperature aging factor, internal resistance degradation factor, and cycle loss factor, respectively, obtained through accelerated aging experiments.

[0196] In this embodiment, the cloud-based operation and maintenance platform includes a third cloud module, used for:

[0197] Based on the remaining lifespan of the problematic batteries, the shortest remaining lifespan of all problematic batteries in the distribution area is taken as the operating lifespan of the distributed energy storage system in the distribution area.

[0198] Based on the operational lifespan of the distributed energy storage system in the transformer area and the location of all problematic batteries within the transformer area, an operation and maintenance strategy is generated and distributed to the edge computing nodes. The distributed energy storage system is then optimized and controlled based on the operation and maintenance strategy.

[0199] It should be noted that, as Figure 3 As shown, the edge computing node transmits the received operation and maintenance strategy as a control command to the energy storage information collection terminal in the distribution area for on-site deployment.

[0200] Example 4

[0201] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0202] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the operation and maintenance method of the distributed energy storage system based on edge-cloud collaboration in the above embodiment.

[0203] Example 5

[0204] Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the edge-cloud collaborative operation and maintenance method for a distributed energy storage system in the above embodiments.

[0205] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0206] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0207] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0208] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims of the present invention.

Claims

1. A method for operation and maintenance of a distributed energy storage system in a transformer substation based on edge-cloud collaboration, characterized in that, include: The system receives real-time operating data of individual cells in the distribution area through edge computing nodes, and performs multi-level edge diagnosis on the real-time operating data based on the calculated dynamic thresholds to diagnose suspected problematic individual cells in the distribution area. The suspected problematic individual battery cells are checked through the cloud-based operation and maintenance platform to identify the problematic batteries in the transformer area and predict their remaining lifespan. Based on the remaining lifespan and location of the problematic batteries, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the distributed energy storage system is optimized and controlled based on the operation and maintenance strategy.

2. The method as described in claim 1, characterized in that, The process of performing multi-level edge diagnosis on the real-time operating data based on the calculated dynamic threshold to diagnose suspected problematic individual cells in the transformer substation includes: Based on the real-time operating data, a dynamic adaptive threshold algorithm is used to compensate for multiple parameters of the reference internal resistance of the individual battery in the distribution area, and the dynamic threshold of the battery internal resistance is calculated; the real-time operating data includes real-time internal resistance, real-time voltage and real-time temperature; The real-time internal resistance of the individual cells in the transformer area is compared with the dynamic threshold of the cell internal resistance to obtain the individual cell-level diagnostic results. When the individual cell-level diagnostic result is abnormal, the real-time voltage of the individual cell in the distribution area is compared with the cluster voltage of the cluster to which the individual cell in the distribution area belongs to perform differential voltage diagnosis to obtain the cluster-level diagnostic result. When the cluster-level diagnostic result is abnormal, the real-time temperature of the individual cell in the distribution area and the stack temperature of the stack where the individual cell in the distribution area is located are compared to obtain the stack-level diagnostic result. When the stack-level diagnostic result is abnormal, the individual cell in the distribution area is identified as a suspected problematic individual cell.

3. The method as described in claim 2, characterized in that, The dynamic threshold of the battery internal resistance is expressed as: Among them, R t R is the dynamic threshold of the battery's internal resistance. b The reference internal resistance is α, the temperature compensation coefficient is α, and T is the real-time temperature of the individual cell in the transformer area. r For reference temperature, α(TT) r ) represents the temperature parameter compensation term, β represents the current fluctuation coefficient, and I r I is the root mean square current of the individual cells in the transformer area. n The rated current of the individual battery cells in the aforementioned distribution area. This is a compensation term for current fluctuation parameters.

4. The method as described in claim 2 or 3, characterized in that, The process of verifying the suspected problematic individual battery cells through a cloud-based operation and maintenance platform, and identifying the problematic batteries in the transformer substation, includes: The cloud-based operation and maintenance platform receives ambient temperature monitoring data from the transformer area's ambient temperature monitoring device for the suspected problematic individual battery cells. When the ambient temperature monitoring data is greater than the set ambient temperature threshold, the suspected problematic single cell battery is determined to be a problematic battery.

5. The method as described in claim 2 or 3, characterized in that, The prediction of the remaining lifespan of the problematic battery includes: Based on the real-time operating data of the problematic battery, a weighted linear model is used to calculate the ratio of the designed life of the problematic battery to multiple weighted summation battery aging factors, thereby obtaining the remaining life of the problematic battery.

6. The method as described in claim 5, characterized in that, The remaining lifespan of the problematic battery is expressed as follows: Where RUL represents the remaining lifespan of the problematic battery; L0 represents the design lifespan of the problematic battery; and T represents the real-time temperature of the problematic battery, ∫(T-25). 2 dt is the temperature aging factor; ΔR is the increase in internal resistance per unit; Δt is the time corresponding to the increase in internal resistance per unit. is the internal resistance degradation factor; EFC is the equivalent number of full cycles at different discharge depths, i.e., the cycle loss factor; a1, a2, and a3 are the weights of the temperature aging factor, internal resistance degradation factor, and cycle loss factor, respectively, obtained through accelerated aging experiments.

7. The method according to any one of claims 1-3, characterized in that, Based on the remaining lifespan and location of the problematic batteries, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the distributed energy storage system is optimized and controlled based on the operation and maintenance strategy, including: Based on the remaining lifespan of the problematic batteries, the shortest remaining lifespan of all problematic batteries in the distribution area is taken as the operating lifespan of the distributed energy storage system in the distribution area. Based on the operational lifespan of the distributed energy storage system in the transformer area and the location of all problematic batteries within the transformer area, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the operation and maintenance strategy is distributed to the edge computing nodes. The distributed energy storage system is then optimized and controlled based on the operation and maintenance strategy.

8. A distributed energy storage system operation and maintenance system for substations based on edge-cloud collaboration, characterized in that, include: The primary diagnostic module is used to receive real-time operating data of individual batteries in the distribution area through edge computing nodes, and perform multi-level edge diagnostics on the real-time operating data based on the calculated dynamic thresholds to diagnose suspected problematic individual batteries in the distribution area. The problem verification module is used to verify the suspected problematic individual battery cells through the cloud-based operation and maintenance platform, identify the problematic batteries in the individual battery cells of the transformer area, and predict the remaining life of the problematic batteries. The strategy generation module is used to generate operation and maintenance strategies through the cloud operation and maintenance platform based on the remaining lifespan and location of the problematic battery, and to optimize and control the distributed energy storage system based on the operation and maintenance strategies.

9. The system as described in claim 8, characterized in that, The primary diagnostic module is specifically used for: Based on the real-time operating data, a dynamic adaptive threshold algorithm is used to compensate for multiple parameters of the reference internal resistance of the individual battery in the distribution area, and the dynamic threshold of the battery internal resistance is calculated; the real-time operating data includes real-time internal resistance, real-time voltage and real-time temperature; The real-time internal resistance of the individual cells in the transformer area is compared with the dynamic threshold of the cell internal resistance to obtain the individual cell-level diagnostic results. When the individual cell-level diagnostic result is abnormal, the real-time voltage of the individual cell in the distribution area is compared with the cluster voltage of the cluster to which the individual cell in the distribution area belongs to perform differential voltage diagnosis to obtain the cluster-level diagnostic result. When the cluster-level diagnostic result is abnormal, the real-time temperature of the individual cell in the distribution area and the stack temperature of the stack where the individual cell in the distribution area is located are compared to obtain the stack-level diagnostic result. When the stack-level diagnostic result is abnormal, the individual cell in the distribution area is identified as a suspected problematic individual cell.

10. The system as described in claim 9, characterized in that, The dynamic threshold of the battery internal resistance is expressed as: Among them, R t R is the dynamic threshold of the battery's internal resistance. b The reference internal resistance is α, the temperature compensation coefficient is α, and T is the real-time temperature of the individual cell in the transformer area. r For reference temperature, α(TT) r ) represents the temperature parameter compensation term, β represents the current fluctuation coefficient, and I r I is the root mean square current of the individual cells in the transformer area. n The rated current of the individual battery cells in the aforementioned distribution area. This is a compensation term for current fluctuation parameters.

11. The system as described in claim 9 or 10, characterized in that, The problem verification module is specifically used for: The cloud-based operation and maintenance platform receives ambient temperature monitoring data from the transformer area's ambient temperature monitoring device for the suspected problematic individual battery cells. When the ambient temperature monitoring data is greater than the set ambient temperature threshold, the suspected problematic single cell battery is determined to be a problematic battery.

12. The system as described in claim 9 or 10, characterized in that, The problem verification module is specifically used for: Based on the real-time operating data of the problematic battery, a weighted linear model is used to calculate the ratio of the designed life of the problematic battery to multiple weighted summation battery aging factors, thereby obtaining the remaining life of the problematic battery.

13. The system as described in claim 12, characterized in that, The remaining lifespan of the problematic battery is expressed as follows: Where RUL represents the remaining lifespan of the problematic battery; L0 represents the design lifespan of the problematic battery; and T represents the real-time temperature of the problematic battery, ∫(T-25). 2 dt is the temperature aging factor; ΔR is the increase in internal resistance per unit; Δt is the time corresponding to the increase in internal resistance per unit. is the internal resistance degradation factor; EFC is the equivalent number of full cycles at different discharge depths, i.e., the cycle loss factor; a1, a2, and a3 are the weights of the temperature aging factor, internal resistance degradation factor, and cycle loss factor, respectively, obtained through accelerated aging experiments.

14. The system according to any one of claims 8-10, characterized in that, The strategy generation module is specifically used for: Based on the remaining lifespan of the problematic batteries, the shortest remaining lifespan of all problematic batteries in the distribution area is taken as the operating lifespan of the distributed energy storage system in the distribution area. Based on the operational lifespan of the distributed energy storage system in the transformer area and the location of all problematic batteries within the transformer area, an operation and maintenance strategy is generated through the cloud-based operation and maintenance platform, and the operation and maintenance strategy is distributed to the edge computing nodes. The distributed energy storage system is then optimized and controlled based on the operation and maintenance strategy.

15. An operation and maintenance system for a distributed energy storage system in a transformer substation based on edge-cloud collaboration, characterized in that, include: The system is sequentially connected to the energy storage information acquisition terminal in the distribution area, the edge computing node, and the cloud operation and maintenance platform. The energy storage information acquisition terminal for the distribution area is used to collect real-time operating data of individual batteries in the distribution area. Edge computing nodes are used to receive real-time operating data of individual cells in the distribution area, and perform multi-level edge diagnosis on the real-time operating data based on the calculated dynamic threshold to diagnose suspected problematic individual cells in the distribution area. The cloud-based operation and maintenance platform is used to verify the suspected problematic individual battery cells, identify the problematic batteries in the distribution area, and predict the remaining lifespan of the problematic batteries. Based on the remaining lifespan and location of the problematic batteries, an operation and maintenance strategy is generated, and the distributed energy storage system is optimized and controlled based on the operation and maintenance strategy.

16. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an operation and maintenance method for a distributed energy storage system based on edge-cloud collaboration as described in any one of claims 1 to 7 is implemented.

17. A readable storage medium, characterized in that, It contains an execution program, which, when executed, implements an operation and maintenance method for a distributed energy storage system based on edge-cloud collaboration as described in any one of claims 1 to 7.

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