Energy storage power station and intelligent early warning method, device and storage medium thereof

Abstract

The present application relates to the field of early warning of energy storage power stations, and proposes an energy storage power station and its intelligent early warning method, device and storage medium. The method includes: constructing a device tree of the energy storage power station, the device tree includes the hierarchical relationship of the devices in the energy storage power station; determining the hierarchical position of a preset early warning model in the device tree; searching for measurement point data used for calculation in the device tree according to the hierarchical position of the early warning model; inputting the measurement point data into the early warning model for early warning calculation to obtain an early warning result. According to the hierarchical position of the early warning model, the required input parameters can be directly searched downward according to the device tree, and early warning calculation can be performed according to the found parameters, which can effectively improve the search speed of the input parameters, improve the efficiency of data acquisition, and facilitate the update and optimization of the early warning algorithm.

Description

Technical Field

[0001] The present application relates to the field of early warning of energy storage power stations, and in particular to an energy storage power station and its intelligent early warning method, device and storage medium. Background Art

[0002] In energy storage power stations, battery equipment used for energy storage is the core equipment of energy storage stations. Intelligent early warning technology for battery equipment is an effective means to ensure the safe operation of energy storage power stations. When conducting intelligent early warning for battery equipment in energy storage power stations, it is usually necessary to set fixed thresholds on the BMS (Battery Management System in Chinese and Battery Manage System in English) to issue an alarm, and report the alarm results to the monitoring background.

[0003] However, since there are many devices in the energy storage power station, for example, there may be tens of thousands of cells in an energy storage power station, and the data collected for each device will also include one or more, making the amount of data collected by the entire energy storage power station very large. When using the early warning algorithm to perform early warning calculations on the equipment in the energy storage power station, it is not conducive to quickly and effectively obtaining the required data, and the efficiency of the early warning calculation is not high. Summary of the invention

[0004] In view of this, the embodiments of the present application provide an energy storage power station and its intelligent early warning method, device and storage medium to solve the problem in the prior art that it is not easy to find the required data in a huge amount of data when performing early warning calculations, resulting in low efficiency in obtaining the data required for calculation during early warning calculations.

[0005] A first aspect of an embodiment of the present application provides an intelligent early warning method for an energy storage power station, the method comprising: constructing a device tree for the energy storage power station, the device tree comprising a hierarchical relationship of devices in the energy storage power station; determining a hierarchical position of a preset early warning model in the device tree; searching for measurement point data for calculation in the device tree according to the hierarchical position of the early warning model; inputting the measurement point data into the early warning model for early warning calculation to obtain an early warning result.

[0006] After constructing a device tree with a hierarchical relationship of devices including energy storage power stations, determine the hierarchical position of the warning model in the device tree based on the input parameters or output parameters in the pre-set warning model, that is, determine which level of the device tree the warning model is located at. If the level includes multiple devices, you can further determine which device in the level the warning model is located at. According to the hierarchical position of the warning model, you can directly search down the device tree for the required input parameters, and perform warning calculations based on the found parameters, which can effectively improve the search speed of the input parameters and improve data acquisition efficiency.

[0007] In a possible implementation of the first aspect, determining the hierarchical position of a preset early warning model in the device tree includes: obtaining the device corresponding to the input parameters of the early warning model; and determining the position of the top-level node of the device in the device tree as the hierarchical position of the early warning model in the device tree.

[0008] When determining the hierarchical position of the warning model, the device of the parameters that need to be input into the warning model can be determined. According to the position of the determined device in the device tree, the position of the top-level node is selected as the hierarchical position of the warning model in the device tree. The warning model is set at the hierarchical position of the top-level node, so that when the warning model determines the input parameters, it can quickly find the required data according to the device tree, which is convenient for improving data search efficiency. It is not limited to determining the hierarchical position of the warning model by input parameters. The hierarchical position of the warning model can also be determined by searching the top-level devices corresponding to the output parameters of the warning model.

[0009] In a possible implementation of the first aspect, according to the hierarchical position of the early warning model, the measurement point data used for calculation is searched in the device tree, including: searching for a computing device according to the hierarchical position of the early warning model, the computing device including a device corresponding to the hierarchical position, or including a device located below the hierarchy; searching for device parameters corresponding to the computing device according to a preset correspondence between the device and the device parameters; and determining the measurement point data corresponding to the searched device parameters according to the correspondence between the preset device parameters and the measurement point data.

[0010] When determining the data used for early warning model calculation, the device can be searched downward based on the hierarchical position of the early warning model in the device tree to determine the device used for early warning model calculation, and the input parameters corresponding to the device can be determined based on the searched device. According to the association between the input parameters and the measurement point data, the measurement point data used for early warning model calculation can be determined, so that the early warning model can perform accurate early warning calculations based on the searched data. When searching for devices related to the early warning model, the devices related to the early warning model can also be pre-set, for example, one or more devices can be set to be related to the early warning model in the early warning model setting interface.

[0011] In a possible implementation of the first aspect, the equipment parameters of the same type of equipment in the energy storage power station adopt the same naming rules, so that the early warning algorithm can use the same equipment parameters to call and calculate the measurement point data at different hierarchical positions, which is convenient for efficiently calling a large amount of data in the energy storage power station and improving data calling efficiency.

[0012] In a possible implementation of the first aspect, before searching for device parameters corresponding to the computing device according to a pre-set correspondence between the device and the device parameters, the method further includes: displaying a selection interface for the computing device; receiving a device selection instruction according to the selection interface, and updating the computing device according to the selection instruction.

[0013] In order to meet the calculation requirements of different warning models for equipment parameters, when configuring the warning model, a selection interface for computing equipment can be included in the configuration interface, and the equipment at the hierarchical position of the warning model can be selected, or all equipment at the hierarchical position can be included. For example, the input parameters of the warning model at the energy storage unit level can include the equipment parameters of the energy storage unit, as well as all equipment under the energy storage unit, including the equipment parameters of the energy storage unit, PCS, battery stack, battery cluster, battery module and battery cell.

[0014] In a possible implementation of the first aspect, the measurement point data is input into the early warning model for early warning calculation to obtain an early warning result, including: inputting the measurement point data into the early warning model for early warning calculation to obtain an early warning value corresponding to the measurement point data; comparing the early warning value with a preset early warning threshold to obtain the early warning result.

[0015] The warning value is calculated by inputting the measurement point data into the preset calculation formula in the warning model. The warning value is compared with the preset warning threshold to determine the warning interval to which the warning value belongs, and the warning result corresponding to the warning interval is generated, including warnings of different levels. Among them, the calculation formula may include, for example, the battery health status calculation formula.

[0016] In a possible implementation of the first aspect, before constructing the device tree of the energy storage power station, the method further includes: determining devices corresponding to the type of the energy storage power station; and determining device parameters corresponding to the devices of the energy storage power station.

[0017] Since different energy storage power stations correspond to different equipment types, it is necessary to determine the type of energy storage power station before determining the equipment tree. For example, the types of energy storage power stations can include centralized energy storage power stations and distributed energy storage power stations. The types of equipment included in centralized energy storage power stations are different from those included in distributed energy storage power stations. After determining the equipment types in the energy storage power station and the equipment parameters corresponding to the equipment types, the logical relationship between the equipment types can be determined based on the main wiring or inclusion relationship of the equipment types of the energy storage power station, and the equipment tree corresponding to the energy storage power station can be determined.

[0018] In a possible implementation of the first aspect, the types of the energy storage power station include centralized energy storage power stations and distributed energy storage power stations, and determining the equipment corresponding to the type of the energy storage power station includes: when the type of the energy storage power station is a centralized energy storage power station, determining that the equipment corresponding to the type of the energy storage power station includes energy storage units, energy storage units, PCS, battery stacks, battery clusters, battery modules and battery cells; when the type of the energy storage power station is a distributed energy storage power station, determining that the equipment corresponding to the type of the energy storage power station includes energy storage units, PCS, battery stacks, battery clusters, battery modules and battery cells.

[0019] For different types of energy storage power stations, the equipment types included in the energy storage power stations are determined accordingly, and a device tree is constructed based on the determined equipment types. The equipment tree corresponding to the energy storage power station is prepared to facilitate the subsequent setting of early warning models and the search for equipment parameters.

[0020] In a possible implementation of the first aspect, determining the equipment parameters corresponding to the equipment of the energy storage power station includes: determining that the equipment parameters corresponding to the equipment of the energy storage power station include static parameters, telemetry parameters, and telesignaling parameters. The static parameters include one or more of the equipment manufacturer, equipment model and specification, equipment date of manufacture, equipment rated voltage, equipment rated current, and equipment rated capacity; the telemetry parameters include one or more of the equipment total voltage, equipment total current, equipment SOC, equipment SOH, equipment monomer maximum voltage value, equipment monomer minimum voltage value, and equipment insulation resistance value; and the telesignaling parameters include one or more of the equipment monomer overvoltage alarm, equipment monomer undervoltage alarm, equipment monomer overtemperature alarm, equipment monomer low temperature alarm, and equipment internal communication failure.

[0021] By determining the static parameters, telemetry parameters and telesignaling parameters in the equipment parameters, the parameters can be used as the input parameters of the early warning model. When configuring in the configuration interface of the early warning model, one or more parameters can be selected from the static parameters, telesignaling parameters and telemetry parameters as the input parameters of the early warning model. For example, telemetry parameters can be selected to determine the real-time status of the equipment, and the equipment can be compared for early warning by comparing the static parameters with the telemetry parameters. The alarm type of the equipment can be determined by the telesignaling parameters, so that specific and reliable early warning information can be output through the early warning model.

[0022] In a possible implementation of the first aspect, when the energy storage power station is a centralized energy storage power station, the nodes of the device tree include energy storage units, energy storage units, PCS, battery stacks, battery clusters, battery modules, and battery cells from the upper level to the lower level. Based on this hierarchical relationship, it is convenient for the early warning model to search for input parameters according to the set hierarchical relationship, so as to improve the data search efficiency of the early warning model.

[0023] In a second aspect, an embodiment of the present application provides an intelligent early warning device for an energy storage power station, the device comprising: an equipment tree construction unit, used to construct an equipment tree of the energy storage power station, the equipment tree comprising a hierarchical relationship of equipment in the energy storage power station; a hierarchical position determination unit, used to determine the hierarchical position of a preset early warning model in the equipment tree; a measurement point data search unit, used to search for measurement point data for calculation in the equipment tree according to the hierarchical position of the early warning model; and an early warning unit, used to input the measurement point data into the early warning model for early warning calculation to obtain an early warning result.

[0024] A third aspect of an embodiment of the present application provides an energy storage power station, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0025] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0026] It can be understood that the effects of the second to fourth aspects mentioned above are basically the same as the effects of the intelligent early warning method for the energy storage power station of the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0028] Figure 1 It is a schematic diagram of the implementation process of an intelligent early warning method for an energy storage power station provided in an embodiment of the present application;

[0029] Figure 2 It is a schematic diagram of a device instantiation of the method provided in an embodiment of the present application;

[0030] Figure 3 This is a schematic diagram of a parameter instantiation interface of the method provided in the embodiment of the present application;

[0031] Figure 4 is a schematic diagram of a device tree of the method provided in an embodiment of the present application;

[0032] Figure 5 A schematic diagram of the implementation flow of input parameters of a warning model provided in an embodiment of the present application;

[0033] Figure 6 is a schematic diagram of an intelligent early warning device for an energy storage power station provided in an embodiment of the present application;

[0034] Figure 7 It is a schematic diagram of an energy storage power station provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0036] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0037] Energy storage power stations are power stations set up to regulate peak and valley electricity consumption. Energy storage power stations include different types of equipment, such as energy storage units, energy storage converters, battery stacks, battery clusters, battery modules and battery cells. In order to ensure the stable and reliable operation of energy storage power stations, it is usually necessary to conduct intelligent early warning of energy storage power stations so as to timely detect operational anomalies in energy storage power stations.

[0038] However, since there are many devices in the energy storage power station, for example, there may be tens of thousands of cells in an energy storage power station, and the data collected for each device will also include one or more, making the amount of data collected by the entire energy storage power station very large. When the early warning algorithm is used to perform early warning calculations on the equipment of the energy storage power station, it is not conducive to quickly and effectively obtaining the required data, and the efficiency of the early warning calculation is not high. Moreover, when the early warning model needs to be iteratively updated, it is troublesome to find the required data, which is not conducive to improving the efficiency of iterative updates.

[0039] Based on this, the embodiment of the present application proposes an intelligent early warning method for an energy storage power station, such as Figure 1 The figure is a schematic diagram of the implementation process of an intelligent early warning method for an energy storage power station provided by an embodiment of the present application, which is described in detail as follows:

[0040] In S101 , a device tree of the energy storage power station is constructed, where the device tree includes a hierarchical relationship of devices in the energy storage power station.

[0041] Specifically, the energy storage power station in the embodiment of the present application may include different types of energy storage power stations. For example, the energy storage power station may include a distributed energy storage power station and a centralized energy storage power station. According to different types of energy storage power stations, the equipment included in the energy storage power station may be determined accordingly.

[0042] The equipment for a centralized energy storage power station may include energy storage units, energy storage units, PCS, battery stacks, battery clusters, battery modules, battery cells and other equipment. For example, in a possible embodiment, for 14 35KV energy storage collection lines, each collection line corresponds to an energy storage unit. 14 energy storage units are created at the energy storage power station level, each energy storage unit includes 6 PCSs, each PCS corresponds to an energy storage unit, 6 energy storage units are created at each energy storage unit level, each energy storage unit level includes a PCS and a battery stack, each battery stack level has 8 battery clusters, each battery cluster level has 5 battery modules, and each battery module level includes 100 battery cells.

[0043] The equipment for distributed energy storage power stations may include energy storage units, PCS, battery stacks, battery clusters, battery modules, battery cells and other equipment.

[0044] After determining the equipment included in the energy storage power station, in order to facilitate the subsequent intelligent early warning calculation, it is necessary to define the equipment parameters included in the equipment in the energy storage power station, so that during the early warning calculation, the equipment parameters can be read to calculate the early warning result.

[0045] The equipment parameters of the equipment in the energy storage power station may include static parameters, telemetry parameters, and telesignaling parameters.

[0046] Among them, the static parameters are parameters determined according to the design information of the equipment, and may include one or more of the equipment manufacturer, equipment model and specifications, equipment production date, equipment rated voltage, equipment rated current, and equipment rated capacity.

[0047] Among them, the equipment manufacturer and the equipment production date can be used to identify the same type of equipment. Parameters such as equipment rated voltage, equipment rated current and equipment rated capacity can be used as input parameters for comparison in the early warning model. For example, the detected equipment parameters can be compared with the rated parameters of the equipment, and the early warning results of the relevant parameters can be determined based on the size relationship between the detected equipment parameters and the rated parameters.

[0048] Among them, the telemetry parameters may include one or more of the total voltage of the device, the total current of the device, the SOC of the device (the full name in English is State of Charge, and the full name in Chinese is the state of charge of the battery), the SOH of the device (the full name in English is State Of Health, and the full name in Chinese is health status), the maximum voltage value of the device cell, the minimum voltage value of the device cell, and the insulation resistance value of the device.

[0049] Telemetry parameters are parameters detected in real time during the operation of the energy storage power station, or parameters calculated based on the detected parameters. For example, during battery charging, the state of charge of the battery corresponding to the first moment t1 is SOC1, and the state of charge of the battery corresponding to the second moment t2 is SOC2. From the first moment t1 to the second moment t2, the amount of electricity charged into the battery is Q 充电 , the charging capacity can be obtained by the ampere-hour integration method:

[0050] Then, the total capacity Q of the battery at this stage is: Q = Q 充电 / (SOC2-SOc1).

[0051] Based on the total capacity of the battery calculated above, combined with the rated capacity in the static parameters of the battery, the battery's state of health SOH can be calculated, that is: SOH = Q / Q 额定 .

[0052] Among them, the remote signal parameters may include intelligent warning information output by the energy storage power station after the warning calculation of the warning algorithm, including one or more of the equipment cell overvoltage alarm, equipment cell undervoltage alarm, equipment cell overtemperature alarm, equipment cell low temperature alarm and equipment internal communication failure.

[0053] When determining the equipment included in the energy storage power station and the corresponding equipment parameters of the equipment, the equipment and equipment parameters in the energy storage power station can be determined according to the pre-set equipment template interface. For example, the equipment included in the energy storage power station can be determined by the energy storage power station type in the equipment template. Figure 2 The device instantiation interface of the energy storage power station shown is used to instantiate the devices in the energy storage power station. For example, the type code, type name, parent code, parent name, and device level of the devices in the energy storage power station can be set.

[0054] After instantiating the equipment in the energy storage power station, you can further set the equipment parameters to complete the parameter instantiation operation of the energy storage power station. Figure 3 The device parameter instantiation interface of the energy storage power station shown in the figure can set the relevant information of the device parameters. Including the device name, parameter code, parameter type, parameter unit, alarm classification corresponding to the parameter, evaluation type, alarm thresholds of different levels, etc. The alarm thresholds of different levels can include severe upper limit, severe lower limit, more severe upper limit, more severe lower limit, and slight upper limit.

[0055] The device tree of an energy storage power station refers to a logical tree with devices as nodes, determined by the main wiring or inclusion relationship of the devices in the energy storage power station. In the device tree, the top-level nodes correspond to the devices with the largest range, and the bottom-level nodes correspond to the devices with the smallest range. For example, for a centralized energy storage power station, its device tree can be as follows Figure 4 As shown, the lower level of the energy storage power station includes energy storage units, the lower level of the energy storage units is the energy storage unit, the lower level of the energy storage unit includes PCS and battery stacks. The lower level of the battery stack includes battery clusters, the lower level of the battery cluster includes battery modules, and the lower level of the battery module includes battery cells. Among them, the battery cell is the lowest level node of the equipment of the energy storage power station.

[0056] Each node in the device tree corresponds to a device in the energy storage power station. Each device includes predefined device parameters, such as static parameters, telesignaling parameters, and telemetry parameters.

[0057] In the embodiment of the present application, in order to improve the computational efficiency of the early warning model, for the same type of equipment, the names of the equipment parameters adopt the same standardized names. For example, for the multiple energy storage units under the energy storage unit, the voltage naming, current naming and other equipment parameters of each energy storage unit all adopt the same standardized names. For example, the voltage name in each battery cluster is the total voltage of the cluster battery, or the parameter name can also be a parameter code, such as CluTotVol.

[0058] For parameter names in the same type of equipment, the same standard naming is used, so that when the early warning model performs parameter search, the corresponding data can be directly found based on the parameter names set in the early warning model and the hierarchical relationship of the device tree. There is no need to modify the names of the input parameters in the early warning model due to different naming adopted by different devices, which can greatly improve the data search efficiency of the early warning calculation.

[0059] In S102, the hierarchical position of the preset warning model in the device tree is determined.

[0060] In the embodiment of the present application, when performing intelligent early warning on the energy storage power station, one or more early warning models can be set, each early warning model can input one or more equipment parameters, and the early warning result is obtained through calculation of the early warning model.

[0061] In general, due to the hierarchical relationship of the device tree, the devices of the upper-level nodes include the devices of the lower-level nodes. Therefore, when performing early warning calculations on the devices of the upper-level nodes, the device parameters of the devices of the lower-level nodes need to be included. For example, the input parameters of the early warning model of the energy storage unit include the device parameters of the energy storage unit itself and the device parameters of the devices of all nodes under the energy storage unit, including the device parameters of all energy storage units under the energy storage unit, the device parameters of the PCS and battery stacks included in all energy storage units under the energy storage unit, the device parameters of the battery cluster in the battery stack included in the energy storage unit under the energy storage unit, and the device parameters of the battery module included in the battery cluster and the device parameters of the battery cells included in the battery module.

[0062] For example, the input parameters of the early warning model at the energy storage unit level usually include the equipment parameters of the energy storage unit itself and all logical devices at the lower level of the energy storage unit, including the equipment parameters of the PCS, battery stack, battery cluster, battery module and battery cell under the energy storage unit.

[0063] Therefore, in a possible implementation method, the hierarchical position of the device whose hierarchical information is at the top level can be determined according to the input parameters in the calculation parameters in the early warning model and the hierarchical information of the device corresponding to the input parameters included, as the hierarchical position of the early warning model in the device tree.

[0064] For example, the input parameters of a certain early warning model include battery cell parameters, battery module parameters, battery cluster parameters, and battery stack parameters. The devices corresponding to the above parameters are battery cells, battery modules, battery clusters, and battery stacks, respectively. According to the determined device tree, the battery cell is at the bottom-level node, the battery module is at the upper level of the battery cell, the battery cluster is at the upper level of the battery module, and the battery stack is at the upper level of the battery cluster. Therefore, the device corresponding to the top-level node in the above input parameters is the battery stack, and the hierarchical position of the early warning model in the device tree is the hierarchical position of the battery stack. According to the determined hierarchical position of the early warning model, the early warning model can be configured or called for each battery stack, that is, the early warning model can be referenced by the corresponding hierarchical device pointers of different energy storage power stations based on its hierarchical position.

[0065] When determining the position of the early warning model, since the early warning model generally combines the equipment parameters of the current level and the equipment parameters of the nodes below the current level to perform early warning calculations for the equipment at the current level, the hierarchical position of the early warning model can also be determined based on the nodes corresponding to the output parameters of the early warning model.

[0066] After the hierarchical position of the warning model is determined, it can be referenced by the device at the defined hierarchical position. In order to define the structured description of the warning model, it includes warning model code, warning model name, warning type, warning model detailed description, warning level, maintenance suggestion, associated algorithm model ID, associated algorithm model name, etc. Among them, the warning model code can be customized, but the warning model code must be unique. The warning type varies according to different devices. For example, for the warning model at the battery cluster level, the warning type can include long-term over-discharge warning, self-discharge abnormality warning, battery cluster SOH warning, etc. For the warning model at the energy storage unit level, the warning type includes SOC error warning, insulation abnormality warning, etc. In addition, the warning type can also be divided into safety and non-safety categories. The warning level can be divided into level 1 warning, level 2 warning, level 3 warning, and level 4 warning according to the severity of the warning event. The algorithm model ID associated with the warning model can be used to replace the version of the algorithm model, or replace a different algorithm to achieve algorithm optimization or iterative update. For example, by changing the algorithm model ID in the warning model, the algorithm model in the warning model is changed to achieve replacement or upgrade of the algorithm model.

[0067] In a possible implementation, the input parameters of the early warning model can also be flexibly configured according to the needs of the early warning model. For example, the input parameters of the early warning model can be configured with one click in the configuration interface of the early warning model, and the configured input parameters can include the device parameters of the equipment at the level where the early warning model is located, and the device parameters of the equipment at all levels below the level. Alternatively, the configured input parameters can include the device parameters of the equipment at the level where the early warning model is located, and the device parameters of the equipment at some levels below the level. Alternatively, the configured input parameters can only include the device parameters of the equipment at the level where the early warning model is located.

[0068] In S103, according to the hierarchical position of the early warning model, the measuring point data used for calculation is searched in the equipment tree.

[0069] The measurement point data is data collected at a preset test point. Different types of measurement point data can be collected through the battery management system, or other sensors can be set to collect sensor data at the arrangement position of the sensor.

[0070] After determining the hierarchical position of the early warning model, the preset device parameters can be searched downwards according to the preset device parameters, combined with the hierarchical position of the early warning model and the device tree. According to the association between the preset device parameters and the measurement point data, the measurement point data used for calculation can be obtained. Specifically, Figure 5 As shown, including:

[0071] In S501, a computing device is searched according to the hierarchical position of the early warning model, and the computing device includes a device corresponding to the hierarchical position, or also includes a device located below the hierarchical position.

[0072] When defining the computing devices related to the warning model with one click, including the devices at the hierarchical position of the warning model and the devices of all nodes below the hierarchical position of the warning model, the computing devices used for warning can be searched accordingly according to the device range of the defined computing devices.

[0073] In a possible implementation, the defined computing devices may only include devices at the level where the early warning model is located, and the devices at the level where the early warning model is located may be searched according to the definition.

[0074] Alternatively, the defined computing devices may include devices at the level where the early warning model is located, as well as individual devices below the level. According to a computing device selection interface provided by the system, a selection instruction for the computing device may be received, and the computing device may be updated based on the selection instruction. For example, the updated computing devices may include devices at this level, as well as individual devices below the level.

[0075] In S502, the device parameters corresponding to the computing device are searched according to the preset correspondence between the device and the device parameters.

[0076] After finding the equipment related to the early warning model calculation, the equipment parameters corresponding to the determined calculation equipment can be obtained according to the predefined correspondence between the equipment and the equipment parameters.

[0077] Since the device parameters of the same type of equipment all use standardized naming, when extracting parameters through the early warning model, the standardized naming can be used to read the relevant data, and there is no need to modify the parameter names read due to different parameter names for the same type of equipment.

[0078] In S503, the measuring point data corresponding to the searched equipment parameter is determined according to the preset correspondence relationship between the equipment parameter and the measuring point data.

[0079] The digital data collection device or system on site of the energy storage power station collects the data of the BMS and PCS measurement points on site. For the point tables of different manufacturers, a one-to-one mapping relationship can be established with the equipment parameters in the equipment tree to associate the equipment parameters with the measurement point data. Based on the associated measurement point data, the early warning model can obtain the specific data of the input parameters.

[0080] For example, in Figure 4 In the device tree shown, it is assumed that the hierarchical position determined by the early warning model 1 is the energy storage unit. Figure 4The energy storage unit 1 and the energy storage unit 2 in the energy storage unit 1 can be configured with the early warning model 1. Assuming that the early warning model 1 is configured in the energy storage unit 1, the device parameters corresponding to the energy storage unit can be searched in the energy storage unit 1 according to the hierarchical position in the early warning model 1, and the corresponding device parameters can be searched for the devices at the lower level of the energy storage unit 1, including PCS1 and PCS2, battery stack 1 and battery stack 2, and the corresponding device parameters can be searched for the devices at the lower level of the battery stack 1 and the battery stack 2, including battery clusters 1-4, and the corresponding device parameters can be searched for the battery modules 1-8 at the lower level of the battery cluster, and the corresponding device parameters can be searched for the battery cells 1-16 at the lower level of the battery modules. Based on the correspondence between the device parameters and the measurement point data, the measurement point data of the input parameters of the early warning model are obtained. Figure 4 The number of devices in the layers is for reference only, and the actual number is determined according to the actual requirements of the energy storage power station.

[0081] In S104, the measurement point data is input into the early warning model to perform early warning calculation to obtain an early warning result.

[0082] According to the algorithm model ID called in the early warning model, combined with the measured point data of the input parameters of the determined algorithm model, the early warning calculation can be performed through the algorithm in the algorithm model to obtain the result of the early warning calculation. The output result of the early warning calculation includes but is not limited to the device name, early warning event description, alarm classification, early warning level, start time, early warning release time, early warning frequency, abnormal value size, etc. In a possible implementation, the early warning result may include early warning details to facilitate understanding of the cause of the early warning, early warning elimination method, early warning output parameter value, etc. The early warning elimination method can provide an elimination method for the same type of early warning results based on historical early warning elimination records, so that staff can handle abnormal early warning results in a timely manner.

[0083] In a possible implementation, the warning result may also include the specific location of the abnormal device. For example, when the input parameters of the warning model include the device parameters of the level where the warning model is located, and the device parameters of all levels below the level, the calculated warning result can determine the specific location of the abnormal device based on the input parameters, including the level where the abnormal device is located, and the specific location of the abnormal device in the level, for example, the specific location of the abnormal device can be identified by the device ID.

[0084] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0085] Figure 6 A schematic diagram of an intelligent early warning device for an energy storage power station provided in an embodiment of the present application, the device comprising:

[0086] A device tree construction unit 601 is used to construct a device tree of the energy storage power station, wherein the device tree includes a hierarchical relationship of devices in the energy storage power station;

[0087] A hierarchical position determination unit 602, configured to determine a hierarchical position of a preset warning model in the device tree;

[0088] A measuring point data searching unit 603 is used to search the measuring point data used for calculation in the device tree according to the hierarchical position of the early warning model;

[0089] The early warning unit 604 is used to input the measurement point data into the early warning model to perform early warning calculation and obtain an early warning result.

[0090] Figure 6 The intelligent early warning device of the energy storage power station shown in Figure 1 The intelligent early warning method of the energy storage power station shown corresponds to this.

[0091] Figure 7 Schematic diagram of an energy storage power station provided by an embodiment of the present application. Figure 7 As shown, the energy storage power station 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70, such as an early warning program of an early warning power station. When the processor 70 executes the computer program 72, the steps in the early warning method embodiments of the early warning power station described above are implemented. Alternatively, when the processor 70 executes the computer program 72, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0092] Exemplarily, the computer program 72 may be divided into one or more modules / units, which are stored in the memory 71 and executed by the processor 70 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 72 in the energy storage power station 7.

[0093] The energy storage power station may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will appreciate that Figure 7 It is only an example of the energy storage power station 7 and does not constitute a limitation on the energy storage power station 7. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the energy storage power station may also include input and output devices, network access devices, buses, etc.

[0094] The processor 70 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0095] The memory 71 may be an internal storage unit of the energy storage power station 7, such as a hard disk or memory of the energy storage power station 7. The memory 71 may also be an external storage device of the energy storage power station 7, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the energy storage power station 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the energy storage power station 7. The memory 71 is used to store the computer program and other programs and data required by the energy storage power station. The memory 71 may also be used to temporarily store data that has been output or is to be output.

[0096] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0097] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0099] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0100] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0102] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0103] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An intelligent early warning method for an energy storage power station, characterized in that: The method comprises: Constructing a device tree of the energy storage power station, wherein the device tree includes a hierarchical relationship of devices in the energy storage power station, and in the hierarchical relationship of the device tree, devices of upper-level nodes include devices of lower-level nodes; Determine the hierarchical position of the preset warning model in the device tree; According to the hierarchical position of the early warning model, searching for the measuring point data for calculation in the equipment tree includes: searching for the computing device according to the hierarchical position of the early warning model, the computing device including the device located below the hierarchy; searching for the device parameter corresponding to the computing device according to the preset correspondence between the device and the device parameter; determining the measuring point data corresponding to the searched device parameter according to the preset correspondence between the device parameter and the measuring point data; The measuring point data is input into the early warning model to perform early warning calculation to obtain an early warning result.

2. The method according to claim 1, characterized in that: Determine the hierarchical position of the preset warning model in the device tree, including: Obtaining the device corresponding to the input parameter of the early warning model; The position of the top-level node of the device in the device tree is determined as the hierarchical position of the early warning model in the device tree.

3. The method according to claim 1, characterized in that Before searching for device parameters corresponding to the computing device according to the preset correspondence between the device and the device parameters, the method further includes: Displaying a selection interface of the computing device; A device selection instruction is received according to the selection interface, and the computing device is updated according to the selection instruction.

4. The method according to claim 1, characterized in that The equipment parameters of the same type of equipment in the energy storage power station adopt the same naming rules.

5. The method according to claim 1, characterized in that Before constructing the equipment tree of the energy storage power station, the method further includes: Determine the equipment corresponding to the type of the energy storage power station; Determine equipment parameters corresponding to equipment of the energy storage power station.

6. The method according to claim 5, characterized in that The types of energy storage power stations include centralized energy storage power stations and distributed energy storage power stations. Determining the equipment corresponding to the type of energy storage power station includes: When the type of the energy storage power station is a centralized energy storage power station, it is determined that the equipment corresponding to the type of the energy storage power station includes an energy storage unit, an energy storage unit, a PCS, a battery stack, a battery cluster, a battery module and a battery cell; When the type of the energy storage power station is a distributed energy storage power station, it is determined that the equipment corresponding to the type of the energy storage power station includes an energy storage unit, a PCS, a battery stack, a battery cluster, a battery module and a battery cell.

7. The method according to claim 5, characterized in that Determining equipment parameters corresponding to equipment of the energy storage power station includes: Determine that the equipment parameters corresponding to the equipment of the energy storage power station include static parameters, telemetry parameters and telesignaling parameters.

8. The method according to claim 7, characterized in that The static parameters include one or more of the equipment manufacturer, equipment model and specifications, equipment factory date, equipment rated voltage, equipment rated current, and equipment rated capacity; the telemetry parameters include one or more of the equipment total voltage, equipment total current, equipment SOC, equipment SOH, equipment single cell maximum voltage value, equipment single cell minimum voltage value, and equipment insulation resistance value; the telesignaling parameters include one or more of the equipment single cell overvoltage alarm, equipment single cell undervoltage alarm, equipment single cell overtemperature alarm, equipment single cell low temperature alarm, and equipment internal communication failure.

9. The method according to claim 1, characterized in that: Inputting the measuring point data into the early warning model to perform early warning calculation to obtain early warning results, including: Inputting the measuring point data into the early warning model to perform early warning calculation to obtain the early warning value corresponding to the measuring point data; The warning value is compared with a preset warning threshold to obtain the warning result.

10. The method according to claim 1, characterized in that When the energy storage power station is a centralized energy storage power station, the nodes of the equipment tree include energy storage units, energy storage units, PCS, battery stacks, battery clusters, battery modules and battery cells in order from the upper level to the lower level.

11. An intelligent early warning device for an energy storage power station, characterized in that: The device comprises: A device tree construction unit, used to construct a device tree of the energy storage power station, wherein the device tree includes a hierarchical relationship of devices in the energy storage power station, and in the hierarchical relationship of the device tree, devices of upper-level nodes include devices of lower-level nodes; A hierarchical position determination unit, used to determine the hierarchical position of a preset warning model in the device tree; The measuring point data search unit is used to search for measuring point data for calculation in the device tree according to the hierarchical position of the early warning model, including: searching for a computing device according to the hierarchical position of the early warning model, the computing device including a device located below the hierarchy; searching for a device parameter corresponding to the computing device according to a preset correspondence between the device and the device parameter; determining the measuring point data corresponding to the searched device parameter according to the preset correspondence between the device parameter and the measuring point data; The early warning unit is used to input the measuring point data into the early warning model to perform early warning calculation and obtain an early warning result.

12. An energy storage power station, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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