Distributed node based energy storage system monitoring method

Abstract

This invention discloses a monitoring method for energy storage systems based on distributed nodes, relating to the field of energy storage system technology. The method includes: acquiring preferred monitoring data for possible weather conditions using environmental detection methods, and identifying direct and indirect monitoring devices for each energy storage node; acquiring a preferred monitoring group for possible weather conditions; and monitoring energy storage nodes based on the direct monitoring devices and the preferred monitoring group. This invention addresses the problem in existing distributed node energy storage system monitoring methods where sensor allocation during parameter acquisition fails to target parameters that significantly impact the operation of the energy storage unit based on its environment. This results in numerous invalid parameter acquisitions, affecting monitoring efficiency, and causing resource waste and data redundancy.

Description

Technical Field

[0001] This invention relates to the field of energy storage system technology, specifically to a monitoring method for energy storage systems based on distributed nodes. Background Technology

[0002] Energy storage systems are devices or systems that store energy in a specific form through technological means and release it when needed. Their core functions are to balance energy supply and demand, improve the utilization rate of renewable energy, and enhance the stability of the power system. Monitoring of energy storage systems is mainly achieved through a combination of hardware data acquisition and software analysis. The core is to track the system status in real time, ensure safe operation, and optimize performance.

[0003] Existing methods for monitoring distributed energy storage systems typically acquire the state and performance parameters of each independent battery cell in the system. They then monitor these parameters and perform parameterized calculations to derive a monitoring strategy. While this improved approach allows for configuration optimization based on parameter analysis to enhance system performance, it lacks the ability to specifically acquire parameters that significantly impact the energy storage cell's operation, based on the cell's environment. This results in a high number of invalid parameters, reducing monitoring efficiency and causing resource waste and data redundancy. For example, patent application CN117811131A discloses a distributed monitoring configuration method and system for energy storage systems. The proposed solution involves regulating the system circulation based on state and performance parameters, employing a combined model approach to parameterize the energy storage configuration strategy algorithm, and generating a local configuration strategy using the independent battery unit as the minimum configuration unit. However, improvements to other energy storage system monitoring methods for distributed nodes typically focus on monitoring and supervision to adapt to various operating states. These improvements still fail to address the issue of sensor allocation during parameter acquisition. They cannot specifically acquire parameters that significantly impact the energy storage unit's operation based on its environment, leading to numerous invalid parameter acquisitions. This negatively impacts monitoring efficiency, resulting in resource waste and data redundancy. Therefore, it is necessary to improve existing energy storage system monitoring methods for distributed nodes. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. By proposing a monitoring method for energy storage systems based on distributed nodes, this invention addresses the issue that existing methods for monitoring energy storage systems based on distributed nodes cannot specifically acquire parameters that have a significant impact on the operation of the energy storage unit based on the environment in which the energy storage unit is located. This results in a large number of invalid parameters being acquired, affecting the monitoring efficiency of the energy storage system, and causing resource waste and data redundancy.

[0005] To achieve the above objectives, this application provides a monitoring method for energy storage systems based on distributed nodes, comprising the following steps: Based on the distributed node planning of the energy storage system, all energy storage nodes are divided into multiple device analysis groups; environmental detection methods are used to detect the energy storage devices of each energy storage node and obtain the weather conditions that can exist; based on the detection results, the optimal monitoring data for each weather condition that can exist is obtained, as well as the direct monitoring devices and indirect monitoring devices for each energy storage node. Based on the preferred monitoring data corresponding to each possible weather condition and the direct and indirect monitoring equipment of each energy storage node, the recorded parameters of the indirect monitoring group and all indirect monitoring groups for each possible weather condition are obtained; the recorded parameters of all indirect monitoring groups are updated, and the updated indirect monitoring group is recorded as the preferred monitoring group. Based on the direct monitoring equipment of each energy storage node and the preferred monitoring group, all energy storage nodes corresponding to the distributed nodes in the energy storage system are monitored.

[0006] Furthermore, based on the distributed node planning of the energy storage system, all energy storage nodes are divided into multiple device analysis groups; environmental monitoring methods are used to monitor the energy storage devices of each energy storage node and obtain the weather conditions that can exist; based on the monitoring results, the optimal monitoring data for each weather condition that can exist is obtained, and the direct and indirect monitoring devices for each energy storage node include: Based on the distributed node planning of the energy storage system, the location of all energy storage devices in the energy storage system is marked on the map; for any energy storage device: based on the location of the energy storage device on the map, the area where the energy storage device is located is recorded as the energy storage area, and the set of weather conditions in the energy storage area in the past week and the average daily wind speed in the past week are recorded as the recent weather characteristics and recent wind speed characteristics of the energy storage device, respectively. Establish k device analysis groups and put energy storage devices with the same recent weather characteristics and recent wind speed characteristics into the same device analysis group. The device analysis group is used to store the names of energy storage devices, k is a positive integer greater than or equal to 1 and less than or equal to n, and n is the number of energy storage devices in the energy storage system. Environmental testing methods were used to analyze each equipment analysis group separately, and the direct and indirect monitoring equipment for each equipment analysis group was obtained based on the analysis results.

[0007] Furthermore, environmental testing methods include: For any energy storage device in the equipment analysis group, all weather types in the energy storage area where the energy storage device is located in the past year are recorded as possible weather. For any possible weather, the environmental data corresponding to the possible weather is obtained based on meteorological data, and the energy storage device is placed in the environment constructed by the environmental data corresponding to the possible weather and operates continuously at power K Th. Temperature sensors, humidity sensors, wind speed sensors, and dust concentration sensors are used to monitor the environment inside the energy storage device in real time. The detected data are recorded as temperature environmental data, humidity environmental data, wind speed environmental data, and dust environmental data, respectively.

[0008] Furthermore, environmental monitoring methods also include: For any one of the following data γ: temperature environment data, humidity environment data, wind speed environment data, and dust environment data corresponding to the energy storage device: establish a plane rectangular coordinate system, denoted as the direct analysis coordinate system, where the units of the X-axis and Y-axis of the direct analysis coordinate system are h and the units of the detected data in data γ, respectively. Under standard conditions, when the energy storage device operates continuously at power K for Th, the data corresponding to the data γ inside the energy storage device is obtained and recorded as standard internal data; based on the standard internal data, the corresponding curve is plotted in the direct analysis coordinate system and recorded as standard internal curve; For any possible weather conditions, based on the parameters corresponding to the data γ obtained by the energy storage device during the possible weather conditions, the corresponding curve is plotted in the direct analysis coordinate system and recorded as the device internal curve. The abscissas of the leftmost and rightmost points of the standard internal curve and the device internal curve are 0 and T, respectively.

[0009] Furthermore, environmental monitoring methods also include: The area between the standard internal curve and the equipment internal curve is recorded as the parameter area corresponding to the data γ of the energy storage equipment under possible weather conditions. The area below the standard internal curve is recorded as the standard area, and the ratio of the parameter area to the standard area is recorded as the standard parameter ratio. Obtain the standard parameter ratios corresponding to temperature, humidity, wind speed, and dust environmental data of energy storage devices under all possible weather conditions.

[0010] Furthermore, environmental monitoring methods also include: For any data γ among temperature environment data, humidity environment data, wind speed environment data, and dust environment data: among the ratios of data γ to all standard parameters corresponding to possible weather conditions, the standard parameter ratio with the largest value is recorded as the maximum influence value, and data γ is recorded as the preferred monitoring data of possible weather conditions corresponding to the maximum influence value. For any possible weather without preferred monitoring data: the data corresponding to the largest standard parameter ratio among the standard parameter ratios of temperature, humidity, wind speed, and dust environmental data during the possible weather is recorded as the preferred monitoring data for the possible weather.

[0011] Furthermore, environmental monitoring methods also include: Based on big data, parameters affected by temperature, humidity, wind speed and dust concentration in energy storage equipment are obtained and recorded as indirect monitoring parameters of temperature, humidity, wind speed and dust concentration respectively. Equipment that monitors temperature, humidity, wind speed, and dust concentration is designated as direct monitoring equipment, while equipment that monitors all indirect monitoring parameters corresponding to energy storage equipment is designated as indirect monitoring equipment.

[0012] Furthermore, based on the preferred monitoring data corresponding to each possible weather condition and the direct and indirect monitoring equipment of each energy storage node, the recorded parameters of the indirect monitoring group for each possible weather condition and all indirect monitoring groups are obtained, including: Let m be the number of possible weather conditions, and establish m indirect monitoring groups. One possible weather condition corresponds to one indirect monitoring group. The indirect monitoring groups are used to store the monitoring data of direct monitoring equipment and indirect monitoring equipment. For any possible weather condition: the parameter corresponding to the preferred monitoring data of the possible weather condition is recorded as the preferred monitoring parameter; the preferred monitoring parameter and all the indirect monitoring parameters corresponding to the preferred monitoring parameter are recorded as the recorded parameters of the indirect monitoring group of the possible weather condition.

[0013] Furthermore, the recorded parameters for all indirect detection groups are updated, and the updated indirect monitoring groups are designated as preferred monitoring groups, including: For any parameter α among temperature, humidity, wind speed, and dust concentration: the indirect monitoring parameter that appears most frequently in all indirect monitoring groups with parameter α as the recording parameter is recorded as the associated indirect parameter of parameter α; all indirect parameter groups with parameter α as the recording parameter but without the associated indirect parameter of parameter α as the recording parameter are recorded as incomplete parameter groups, and the associated indirect parameter of parameter α is added to the recording parameters of all incomplete parameter groups; The associated indirect parameters corresponding to temperature, humidity, wind speed, and dust concentration are obtained, and the associated indirect parameters are added to the incomplete parameter group. All the indirect monitoring groups of possible weather conditions are recorded as the preferred monitoring groups of all possible weather conditions.

[0014] Furthermore, based on the direct monitoring equipment of each energy storage node and the preferred monitoring group, monitoring of all energy storage nodes corresponding to the distributed nodes in the energy storage system includes: When any energy storage device in a distributed energy storage system is in any possible weather β corresponding to the energy storage device, the recorded parameters corresponding to the preferred monitoring group of the possible weather are recorded as the parameters to be monitored. When the energy storage device is in a weather condition β, all parameters to be monitored are monitored using both direct and indirect monitoring devices.

[0015] The beneficial effects of this invention are as follows: Firstly, based on the distributed node planning of the energy storage system, all energy storage nodes are divided into multiple device analysis groups. Environmental monitoring methods are used to detect the energy storage devices of each energy storage node and obtain the possible weather conditions. Based on the detection results, the preferred monitoring data for each possible weather condition, as well as the direct and indirect monitoring devices for each energy storage node, are obtained. The advantage of this is that by obtaining the possible weather conditions corresponding to the energy storage devices of each energy storage node and the preferred monitoring data for each possible weather condition, the data corresponding to the parameters of the energy storage devices that are significantly affected by the possible weather conditions can be obtained when the energy storage node is in different possible weather conditions. This ensures that the data monitoring of the indirect monitoring devices of the energy storage node is determined by the monitoring results of the direct monitoring devices, thereby enabling targeted acquisition of parameters that have a significant impact on the energy storage unit during operation, thus solving the problem of obtaining too many invalid parameters. This application also obtains the recorded parameters of the indirect monitoring group and all indirect monitoring groups for each possible weather condition based on the preferred monitoring data corresponding to each possible weather condition and the direct and indirect monitoring devices of each energy storage node; updates the recorded parameters of all indirect monitoring groups and records the updated indirect monitoring groups as preferred monitoring groups; finally, based on the direct monitoring devices and preferred monitoring groups of each energy storage node, monitors all energy storage nodes corresponding to the distributed nodes in the energy storage system. The advantage of this is that by obtaining the preferred monitoring group for each possible weather condition, the parameters that should be monitored for each energy storage node under each possible weather condition can be obtained, thereby ensuring that the parameters collected are all parameters that can reflect the impact of possible weather conditions on the operation of energy storage nodes, thereby improving the monitoring efficiency of the energy storage system and avoiding resource waste and data redundancy. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the method of the present invention; Figure 2 This is a schematic diagram of the direct analysis coordinate system of the present invention; Figure 3 This is a schematic diagram illustrating the relationship between the storage node, the weather conditions that can exist, and the indirect monitoring group of the present invention. Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, this application provides a monitoring method for energy storage systems based on distributed nodes, including the following steps: Step S1: Based on the distributed node planning of the energy storage system, all energy storage nodes are divided into multiple device analysis groups; environmental detection methods are used to detect the energy storage devices of each energy storage node and obtain the weather conditions that can exist; based on the detection results, the preferred monitoring data for each weather condition that can exist is obtained, as well as the direct monitoring devices and indirect monitoring devices for each energy storage node. Step S1 includes: Step S101, based on the distributed node planning of the energy storage system, marking the location of all energy storage devices in the energy storage system on the map; for any energy storage device: based on the location of the energy storage device on the map, the area where the energy storage device is located is recorded as the energy storage area, and the set of weather conditions in the energy storage area in the past week and the average daily wind speed in the past week are recorded as the recent weather characteristics and recent wind speed characteristics of the energy storage device, respectively. In practical implementation, for example, during a data analysis, if the weather pattern of an energy storage area over the past week is rainy, sunny, rainy, rainy, rainy, cloudy, and foggy, then the recent weather characteristics can be set as [rainy, sunny, rainy, rainy, rainy, cloudy, foggy]. By obtaining the recent weather characteristics and recent wind speed characteristics respectively, after establishing the equipment analysis group, energy storage devices in the same external environment can be planned into the same equipment analysis group. This way, when analyzing energy storage devices in the same equipment analysis group, only one energy storage device needs to be analyzed, and the corresponding preferred monitoring group for the weather conditions can be obtained, thereby improving the efficiency of data analysis. Step S102: Establish k device analysis groups and put energy storage devices with the same recent weather characteristics and recent wind speed characteristics into the same device analysis group. The device analysis group is used to store the name of the energy storage device, k is a positive integer greater than or equal to 1 and less than or equal to n, and n is the number of energy storage devices in the energy storage system. In the specific implementation process, the value of k can be set by the number of energy storage devices with different recent weather characteristics and recent wind speed characteristics after actual data processing. Step S103: Use environmental detection methods to analyze each equipment analysis group, and obtain the direct monitoring equipment and indirect monitoring equipment for each equipment analysis group based on the analysis results.

[0019] The environmental monitoring method includes: step S1031, for any energy storage device in the equipment analysis group, all weather types in the energy storage area where the energy storage device is located in the past year are recorded as possible weather; for any possible weather, the environmental data corresponding to the possible weather is obtained based on meteorological data, and the energy storage device is placed in the environment constructed by the environmental data corresponding to the possible weather and continuously operates Th with power K; In the specific implementation process, the value of K can be the rated power of the energy storage device during operation, and T can be determined according to the actual duration of data collection. By controlling the energy storage device to operate continuously at power K for Th, the actual operating state of the energy storage device in possible weather conditions can be simulated, thereby making the subsequent data collection more realistic. In this embodiment, the value of T is set to 24, and the energy storage device is placed in an environment built from the environmental data corresponding to possible weather conditions and operates continuously at power K for one day. Step S1032: Use temperature sensor, humidity sensor, wind speed sensor and dust concentration sensor to monitor the environment inside the energy storage device in real time, and record the detected data as temperature environment data, humidity environment data, wind speed environment data and dust environment data respectively.

[0020] In this embodiment, the parameters detected by the temperature sensor, humidity sensor, wind speed sensor, and dust concentration sensor are temperature, humidity, wind speed, and dust concentration, respectively, by default. The environmental detection method also includes: step S1033, for any one of the following data γ: temperature environmental data, humidity environmental data, wind speed environmental data and dust environmental data corresponding to the energy storage device: establish a plane rectangular coordinate system, denoted as the direct analysis coordinate system, wherein the units of the X-axis and Y-axis of the direct analysis coordinate system are h and the units of the detected data in data γ, respectively. Step S1034: Obtain the data corresponding to the data γ inside the energy storage device when the energy storage device continuously operates at power K Th under standard conditions, and record it as standard internal data; draw the corresponding curve in the direct analysis coordinate system based on the standard internal data, and record it as standard internal curve. In the specific implementation process, the standard state is the operating state of the energy storage device when it is not affected by the external environment and all operating parameters remain normal. Step S1035: For any possible weather conditions, based on the parameters corresponding to the data γ obtained by the energy storage device when it is in a possible weather condition, draw the corresponding curve in the direct analysis coordinate system and record it as the device internal curve. The abscissas of the leftmost and rightmost points of the standard internal curve and the device internal curve are 0 and T, respectively.

[0021] The environmental testing method also includes: step S1036, which records the area between the standard internal curve and the equipment internal curve as the parameter area corresponding to the data γ of the energy storage device under possible weather conditions, records the area below the standard internal curve as the standard area, and records the ratio of the parameter area to the standard area as the standard parameter ratio. Step S1037: Obtain the standard parameter ratios corresponding to temperature, humidity, wind speed, and dust environmental data of the energy storage device under all possible weather conditions.

[0022] In this embodiment, the larger the area of ​​the standard parameter ratio corresponding to data γ under possible weather conditions, the greater the influence of possible weather on data γ. That is, the greater the difference between the parameter corresponding to data γ under possible weather conditions and the parameter under standard conditions. For example, in a data analysis, the parameter areas corresponding to humidity of the energy storage device under rainy and sunny weather conditions are as follows: Figure 2 As shown in CQ1 and CQ2, and curve BN is the standard internal curve, the analysis shows that the area of ​​CQ1 is greater than that of CQ2, that is, the ratio of the standard parameters corresponding to CQ1 is greater than that corresponding to CQ2. This indicates that the humidity inside the energy storage device is greatly affected during rainy days. Therefore, in subsequent analysis, if CQ1 has the largest area in the parameter region corresponding to humidity and all possible weather conditions, humidity can be recorded as the preferred monitoring data for rainy days. The environmental monitoring method also includes: step S1038, for any data γ among temperature environmental data, humidity environmental data, wind speed environmental data and dust environmental data: among the ratios of data γ to all standard parameters corresponding to possible weather conditions, the standard parameter ratio with the largest value is recorded as the maximum influence value, and data γ is recorded as the preferred monitoring data of possible weather conditions corresponding to the maximum influence value; Step S1039: For any possible weather without preferred monitoring data: when the weather is in the possible weather, the data corresponding to the standard parameter ratio with the largest value among the standard parameter ratios of temperature environmental data, humidity environmental data, wind speed environmental data and dust environmental data is recorded as the preferred monitoring data of the possible weather. In the specific implementation process, for example, during a data analysis, it is found that there is no preferred monitoring data on sunny days, and the standard parameter ratios of temperature, humidity, wind speed, and dust environmental data corresponding to sunny days are 1.3, 0.5, 1.1, and 0.8, respectively. This indicates that on sunny days, among temperature, humidity, wind speed, and dust, temperature has the greatest impact on energy storage equipment. Therefore, temperature can be used as the preferred monitoring data on sunny days. Step S10310: Based on big data, obtain the parameters of the energy storage device that are affected by temperature, humidity, wind speed and dust concentration respectively, and record them as indirect monitoring parameters of temperature, humidity, wind speed and dust concentration respectively. In the specific implementation process, the indirect monitoring parameters can be determined according to the actual data that can be monitored inside the energy storage device. For example, high temperature or high humidity can cause electrode corrosion, so the indirect monitoring parameters corresponding to temperature and humidity can include the internal resistance of the battery cell. Also, environmental dust can cause blockage of heat dissipation channels, so the indirect monitoring parameters of dust concentration can include the speed of the cooling fan. Step S10311: Devices that monitor temperature, humidity, wind speed and dust concentration are designated as direct monitoring devices, and devices that monitor all indirect monitoring parameters corresponding to the energy storage device are designated as indirect monitoring devices.

[0023] Step S2: Based on the preferred monitoring data corresponding to each possible weather and the direct and indirect monitoring devices of each energy storage node, obtain the recorded parameters of the indirect monitoring group for each possible weather and all indirect monitoring groups; update the recorded parameters of all indirect monitoring groups, and record the updated indirect monitoring group as the preferred monitoring group; Step S2 includes: Please refer to Figure 3 As shown, in step S201, the number of possible weather conditions is recorded as m, and m indirect monitoring groups are established. One possible weather condition corresponds to one indirect monitoring group. The indirect monitoring group is used to store the monitoring data of the direct monitoring equipment and the indirect monitoring equipment. Step S202: For any possible weather: Record the parameters corresponding to the preferred monitoring data of the possible weather as preferred monitoring parameters; record the preferred monitoring parameters and all the indirect monitoring parameters corresponding to the preferred monitoring parameters as the recorded parameters of the indirect monitoring group of the possible weather. In the specific implementation process, for example, during a data analysis, the preferred monitoring data for rainy days is humidity, and all the indirect monitoring parameters of humidity are the system-to-ground insulation resistance and the internal resistance of the battery cell. Therefore, humidity, system-to-ground insulation resistance, and battery cell internal resistance can be recorded as the recorded parameters of the indirect monitoring group for rainy days.

[0024] Step S2 further includes: Step S203, for any parameter α among temperature, humidity, wind speed and dust concentration: the indirect monitoring parameter that appears most frequently in all indirect monitoring groups with parameter α as the recording parameter is recorded as the associated indirect parameter of parameter α; all indirect parameter groups with parameter α as the recording parameter but not with the associated indirect parameter of parameter α as the recording parameter are recorded as incomplete parameter groups, and the associated indirect parameter of parameter α is added to the recording parameters of all incomplete parameter groups; In the specific implementation process, for example, after data processing, if the associated indirect parameter of humidity is found to be the internal resistance of a battery cell, then all recorded parameters that contain humidity but do not contain battery cell internal resistance are recorded as incomplete parameter groups, and battery cell internal resistance is added to the incomplete parameter groups. The purpose is that, since battery cell internal resistance is an associated indirect parameter of humidity, it indicates that among all indirect monitoring parameters, battery cell internal resistance has the strongest correlation with humidity. Therefore, when monitoring humidity, battery cell internal resistance can be monitored together, so as to analyze the humidity monitoring data more comprehensively. Step S204: Obtain the associated indirect parameters corresponding to temperature, humidity, wind speed and dust concentration, and add the associated indirect parameters to the incomplete parameter group to obtain all the indirect monitoring groups of possible weather conditions, which are recorded as the preferred monitoring groups of all possible weather conditions. In the specific implementation process, if after analysis, the indirect parameter group is not recorded as a missing parameter group, that is, no accompanying indirect parameters are added to the indirect monitoring group, it indicates that the parameter types corresponding to the data stored in the indirect monitoring group are relatively comprehensive. Therefore, the indirect parameter group can be directly recorded as the preferred parameter group.

[0025] Step S3: Based on the direct monitoring equipment of each energy storage node and the preferred monitoring group, monitor all energy storage nodes corresponding to the distributed nodes in the energy storage system. Step S3 includes: Step S301, when any energy storage device in the distributed energy storage system is in any possible weather β corresponding to the energy storage device, the recorded parameters corresponding to the preferred monitoring group corresponding to the possible weather are recorded as the parameters to be monitored. Step S302, when the energy storage device is in a weather condition β where it is possible to exist, use direct monitoring equipment and indirect monitoring equipment to monitor all parameters to be monitored.

[0026] Example 2, please refer to Figure 4 As shown, Figure 4A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps as described in the distributed node-based energy storage system monitoring method to achieve the following functions: First, based on the distributed node planning of the energy storage system, all energy storage nodes are divided into multiple device analysis groups; environmental detection methods are used to detect the energy storage devices of each energy storage node and obtain the possible weather conditions; based on the detection results, the preferred monitoring data for each possible weather condition, as well as the direct and indirect monitoring devices for each energy storage node, are obtained; then, based on the preferred monitoring data corresponding to each possible weather condition and the direct and indirect monitoring devices for each energy storage node, the indirect monitoring group for each possible weather condition and the recorded parameters of all indirect monitoring groups are obtained; the recorded parameters of all indirect monitoring groups are updated, and the updated indirect monitoring group is recorded as the preferred monitoring group; finally, based on the direct monitoring devices and preferred monitoring groups of each energy storage node, all energy storage nodes corresponding to the distributed nodes in the energy storage system are monitored.

[0027] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0028] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the distributed node-based energy storage system monitoring method provided by the above methods. The method includes: firstly, based on the distributed node planning of the energy storage system, dividing all energy storage nodes into multiple device analysis groups; using an environmental detection method to detect the energy storage devices of each energy storage node and obtain the possible weather conditions; based on the detection results, obtaining the preferred monitoring data for each possible weather condition, as well as the direct monitoring devices and indirect monitoring devices of each energy storage node; then, based on the preferred monitoring data corresponding to each possible weather condition and the direct monitoring devices and indirect monitoring devices of each energy storage node, obtaining the indirect monitoring group for each possible weather condition and the recorded parameters of all indirect monitoring groups; updating the recorded parameters of all indirect monitoring groups, and recording the updated indirect monitoring group as the preferred monitoring group; finally, based on the direct monitoring devices and preferred monitoring groups of each energy storage node, monitoring all energy storage nodes corresponding to the distributed nodes in the energy storage system.

[0029] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-described distributed node-based energy storage system monitoring method to achieve the following functions: First, based on the distributed node planning of the energy storage system, all energy storage nodes are divided into multiple device analysis groups; environmental detection methods are used to detect the energy storage devices of each energy storage node and obtain the possible weather conditions; based on the detection results, the preferred monitoring data for each possible weather condition, as well as the direct monitoring devices and indirect monitoring devices of each energy storage node, are obtained; then, based on the preferred monitoring data corresponding to each possible weather condition and the direct and indirect monitoring devices of each energy storage node, the indirect monitoring group for each possible weather condition and the recorded parameters of all indirect monitoring groups are obtained; the recorded parameters of all indirect monitoring groups are updated, and the updated indirect monitoring group is recorded as the preferred monitoring group; finally, based on the direct monitoring devices and preferred monitoring groups of each energy storage node, all energy storage nodes corresponding to the distributed nodes in the energy storage system are monitored.

[0030] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0031] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for monitoring an energy storage system based on distributed nodes, characterized in that, Includes the following steps: Based on the distributed node planning of the energy storage system, all energy storage nodes are divided into multiple device analysis groups. For any energy storage device in the device analysis group, all weather types in the energy storage area where the energy storage device is located in the past year are recorded as possible weather. For any possible weather, the environmental data corresponding to the possible weather is obtained based on meteorological data, and the energy storage device is placed in the environment built by the environmental data corresponding to the possible weather and operates continuously at power K Th. Temperature sensors, humidity sensors, wind speed sensors, and dust concentration sensors are used to monitor the environment inside the energy storage device in real time. The detected data are recorded as temperature environment data, humidity environment data, wind speed environment data, and dust environment data, respectively. For any one of the following data γ: temperature environment data, humidity environment data, wind speed environment data, and dust environment data corresponding to the energy storage device: establish a plane rectangular coordinate system, denoted as the direct analysis coordinate system, where the units of the X-axis and Y-axis of the direct analysis coordinate system are h and the units of the detected data in data γ, respectively. Under standard conditions, when the energy storage device operates continuously at power K for Th, the data corresponding to the data γ inside the energy storage device is obtained and recorded as standard internal data; based on the standard internal data, the corresponding curve is plotted in the direct analysis coordinate system and recorded as standard internal curve; For any possible weather conditions, based on the parameters corresponding to the data γ obtained by the energy storage device during the possible weather conditions, the corresponding curve is plotted in the direct analysis coordinate system and recorded as the internal curve of the device. The area between the standard internal curve and the equipment internal curve is recorded as the parameter area corresponding to the data γ of the energy storage equipment under possible weather conditions. The area below the standard internal curve is recorded as the standard area, and the ratio of the parameter area to the standard area is recorded as the standard parameter ratio. Among the ratios of data γ to all possible weather conditions corresponding to standard parameters, the ratio with the largest value is recorded as the maximum impact value, and data γ is recorded as the preferred monitoring data for possible weather conditions corresponding to the maximum impact value. Based on big data, parameters affected by temperature, humidity, wind speed and dust concentration in energy storage equipment are obtained and recorded as indirect monitoring parameters of temperature, humidity, wind speed and dust concentration respectively. Equipment that monitors temperature, humidity, wind speed and dust concentration is categorized as direct monitoring equipment, while equipment that monitors all indirect monitoring parameters corresponding to energy storage equipment is categorized as indirect monitoring equipment. Let m be the number of possible weather conditions, and establish m indirect monitoring groups. One possible weather condition corresponds to one indirect monitoring group. The indirect monitoring groups are used to store the monitoring data of direct monitoring equipment and indirect monitoring equipment. For any possible weather: the parameter corresponding to the preferred monitoring data of the possible weather is recorded as the preferred monitoring parameter; the preferred monitoring parameter and all the indirect monitoring parameters corresponding to the preferred monitoring parameter are recorded as the recorded parameters of the indirect monitoring group of the possible weather. Update the recorded parameters for all indirect detection groups, and designate the updated indirect monitoring groups as the preferred monitoring groups; Based on the direct monitoring equipment of each energy storage node and the preferred monitoring group, all energy storage nodes corresponding to the distributed nodes in the energy storage system are monitored.

2. The energy storage system monitoring method based on distributed nodes according to claim 1, characterized in that, Based on the distributed node planning of the energy storage system, all energy storage nodes are divided into multiple device analysis groups; environmental monitoring methods are used to monitor the energy storage devices of each energy storage node and obtain the weather conditions that can exist; based on the monitoring results, the optimal monitoring data for each weather condition that can exist is obtained, and the direct and indirect monitoring devices for each energy storage node include: Based on the distributed node planning of the energy storage system, the location of all energy storage devices in the energy storage system is marked on the map; for any energy storage device: based on the location of the energy storage device on the map, the area where the energy storage device is located is recorded as the energy storage area, and the set of weather conditions in the energy storage area in the past week and the average daily wind speed in the past week are recorded as the recent weather characteristics and recent wind speed characteristics of the energy storage device, respectively. Establish k device analysis groups, and put energy storage devices with the same recent weather characteristics and recent wind speed characteristics into the same device analysis group. The device analysis group is used to store the names of energy storage devices, k is a positive integer greater than 1 and less than or equal to n, and n is the number of energy storage devices in the energy storage system. Environmental testing methods were used to analyze each equipment analysis group separately, and the direct and indirect monitoring equipment for each equipment analysis group was obtained based on the analysis results.

3. The energy storage system monitoring method based on distributed nodes according to claim 2, characterized in that, Update the recorded parameters for all indirect detection groups, and designate the updated indirect monitoring groups as preferred monitoring groups, including: For any parameter α among temperature, humidity, wind speed, and dust concentration: the indirect monitoring parameter that appears most frequently in all indirect monitoring groups with parameter α as the recording parameter is recorded as the associated indirect parameter of parameter α; all indirect parameter groups with parameter α as the recording parameter but without the associated indirect parameter of parameter α as the recording parameter are recorded as incomplete parameter groups, and the associated indirect parameter of parameter α is added to the recording parameters of all incomplete parameter groups; The associated indirect parameters corresponding to temperature, humidity, wind speed, and dust concentration are obtained, and the associated indirect parameters are added to the incomplete parameter group. All the indirect monitoring groups of possible weather conditions are recorded as the preferred monitoring groups of all possible weather conditions.

4. The energy storage system monitoring method based on distributed nodes according to claim 3, characterized in that, Based on the direct monitoring equipment of each energy storage node and the optimized monitoring group, the monitoring of all energy storage nodes corresponding to the distributed nodes in the energy storage system includes: When any energy storage device in a distributed energy storage system is in any possible weather β corresponding to the energy storage device, the recorded parameters corresponding to the preferred monitoring group of the possible weather are recorded as the parameters to be monitored. When the energy storage device is in a weather condition β, all parameters to be monitored are monitored using both direct and indirect monitoring devices.

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