Carbon dioxide energy storage system state assessment method based on operation data

Abstract

The application relates to the technical field of carbon dioxide energy storage, and discloses a carbon dioxide energy storage system state evaluation method based on operation data, which comprises the following steps: obtaining operation data of a carbon dioxide energy storage system, and synchronously processing each operation data to form an operation data set; based on the flow path of carbon dioxide and the operation data set, establishing a corresponding relationship between the operation data of adjacent devices to form operation correlation data; performing change statistics on the operation correlation data to form correlation change information; performing consistency judgment on the operation data with the corresponding relationship according to each correlation change information; when the change trends of the operation data with the corresponding relationship are inconsistent, generating abnormal correlation information; determining the operation states between each device according to the abnormal correlation information and the flow path; and generating a state evaluation result of the carbon dioxide energy storage system according to the operation states. The application realizes system-level state evaluation and locates the range of abnormal influence.

Description

Technical Field

[0001] This invention relates to the field of carbon dioxide energy storage technology, and more specifically, to a method for assessing the status of a carbon dioxide energy storage system based on operational data. Background Technology

[0002] Carbon dioxide energy storage technology is a novel energy storage method that utilizes the thermodynamic properties of carbon dioxide as a working medium during compression, storage, and expansion to release energy. Compared to traditional compressed air energy storage technology, carbon dioxide energy storage systems have advantages such as better working medium circulation performance, higher energy density, and stronger adaptability, making them effective for applications in new energy consumption, grid peak shaving, and large-scale energy storage.

[0003] Existing patent CN120524287A discloses a multi-dimensional monitoring and early warning method and system for an adsorption-compression carbon dioxide energy storage system. It collects inlet and outlet pressure, temperature, and carbon dioxide flow data of the compression equipment using pressure sensors, temperature sensors, and flow sensors. It also collects storage pressure, temperature, and adsorption capacity changes data using tank monitoring equipment. Combined with a thermodynamic model, it analyzes compression efficiency, heat exchange performance, and energy release efficiency. Machine learning algorithms are introduced to extract features and predict risks from the collected multi-source operating data, and different early warning levels are assigned based on risk indicators, enabling abnormal monitoring and safety control of the energy storage system. However, carbon dioxide energy storage systems involve multiple stages, and there is a continuous carbon dioxide flow relationship between the equipment in each stage. Existing methods mainly judge whether the parameters of individual equipment exceed set thresholds, lacking means to analyze the correlation and changes between upstream and downstream equipment operating data based on the actual carbon dioxide flow path, making it difficult to detect abnormalities in equipment coordination in a timely manner.

[0004] Therefore, it is necessary to design a carbon dioxide energy storage system state assessment method based on operational data to address the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a carbon dioxide energy storage system status assessment method based on operational data, which aims to solve the problem of the lack of means to analyze the correlation and change relationship between the operational data of upstream and downstream equipment based on the actual flow path of carbon dioxide, making it difficult to detect abnormal coordination between equipment in a timely manner.

[0006] This invention proposes a method for assessing the state of a carbon dioxide energy storage system based on operational data, comprising: The system acquires operational data from a carbon dioxide energy storage system, including compression operational data, heat exchange operational data, storage operational data, and energy release operational data; and performs synchronous processing on each of the operational data to form an operational dataset. Based on the flow path of carbon dioxide and the operational dataset, a correspondence is established between the operational data of adjacent devices to form operational correlation data; changes in the operational correlation data are statistically analyzed to form correlation change information. Based on the aforementioned correlation change information, a consistency judgment is made on the operational data that have the corresponding relationship; when the change trends of the operational data that have the corresponding relationship are inconsistent, abnormal correlation information is generated. Based on the anomaly correlation information and the flow path, the operating status of each device is determined; based on the operating status, a status assessment result of the carbon dioxide energy storage system is generated.

[0007] Furthermore, the compression operation data includes inlet pressure data, outlet pressure data, compression power data, and carbon dioxide flow data of the compression equipment; the heat exchange operation data includes inlet temperature data, outlet temperature data, and heat exchange medium operation data of the heat exchange equipment; the storage operation data includes internal pressure data, internal temperature data, and storage capacity data of the storage equipment; and the energy release operation data includes inlet pressure data, outlet pressure data, and output power data of the energy release equipment.

[0008] Furthermore, the synchronization processing of the aforementioned operational data includes: Obtain the data acquisition time information corresponding to each of the aforementioned operational data; perform time matching on the operational data acquired by different devices based on the data acquisition time information; combine the operational data of each device within the same time range to form the operational dataset.

[0009] Furthermore, when generating operational related data, it includes: Based on the flow direction of carbon dioxide, the upstream and downstream relationships between various devices are determined; the operating data on the output side of the upstream device is matched with the operating data on the input side of the downstream device to establish the correspondence between adjacent devices and form the operating association data.

[0010] Furthermore, when forming related change information, it includes: Obtain the change in the operating data of adjacent devices in the operating association data; statistically analyze the change direction, change magnitude, and change time of the operating data respectively; and generate the association change information of the operating data association change situation based on the statistical results.

[0011] Furthermore, when performing a consistency check on the operational data that have the aforementioned correspondence, the process includes: Obtain a preset correlation change relationship of the operating data between adjacent devices; match the correlation change information with the preset correlation change relationship; when the correlation change information does not satisfy the correlation change relationship, determine that the operating data change trends of the corresponding relationship are inconsistent.

[0012] Furthermore, when generating abnormal correlation information, it includes: Identify the device identifiers and operational data of adjacent devices with inconsistent trends of change; generate the abnormal association information based on the device identifiers and operational data.

[0013] Furthermore, determining the operating status of each device includes: Based on the flow direction of carbon dioxide along the flow path, the abnormal association information is sorted; the device identifier and abnormal propagation range of the abnormal association information are determined; based on the abnormal propagation range, the operating status of each device and the cooperative operating status between devices are determined.

[0014] Furthermore, when generating the state assessment results, the following are included: Based on the operating status of each device, the overall operating status of the carbon dioxide energy storage system is determined; when there is no abnormal correlation information between adjacent devices, the carbon dioxide energy storage system is determined to be in normal operating status; when there is continuous abnormal correlation information between adjacent devices, the carbon dioxide energy storage system is determined to be in abnormal operating status, and an abnormal device identifier is output.

[0015] Furthermore, it also includes: marking the operating data of the devices with the abnormal association information according to the status assessment results; and storing the abnormal association information, the device identifier, and the operating status of each device in association to form historical status assessment data.

[0016] Compared with existing technologies, the advantages of this invention are as follows: Based on the actual flow path of carbon dioxide in the energy storage system, a corresponding relationship is established between the operating data of adjacent devices to form operational correlation data. This goes beyond judging the status of a single device parameter; instead, by analyzing the operational changes during the transfer of carbon dioxide from upstream to downstream devices, a comprehensive analysis of the coordinated operational status between compression, heat exchange, storage, and energy release is achieved, improving the ability to perceive the overall operational status. By statistically analyzing the changes in operational correlation data, the direction, magnitude, and time of change of adjacent device operating data are obtained, and the correlation change information is used to determine whether the corresponding operating data meet a preset change relationship. When inconsistent change trends occur between adjacent devices, abnormal correlation information can be generated in a timely manner, thereby identifying hidden faults where individual device parameters do not significantly exceed limits but the operational relationship between devices has already become abnormal, improving the early detection and accuracy of anomaly detection. Combining the abnormal correlation information with the carbon dioxide flow path, the location of the devices involved in the abnormal information and the scope of anomaly propagation are analyzed to determine the operational status of each device and the coordinated operational status between devices, realizing the transformation from single-device anomaly identification to system-level status assessment and locating the scope of anomaly impact. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of a carbon dioxide energy storage system state assessment method based on operational data provided in an embodiment of the present invention; Figure 2 A logical flowchart for evaluating the state of a carbon dioxide energy storage system based on operational data, provided for embodiments of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] In some embodiments of this application, see Figure 1-2As shown, a method for assessing the state of a carbon dioxide energy storage system based on operational data is proposed, including: S100: Acquire operational data of the carbon dioxide energy storage system, including compression operational data, heat exchange operational data, storage operational data, and energy release operational data; and synchronously process each operational data to form an operational dataset; S200: Based on the flow path and operation dataset of carbon dioxide, establish a correspondence between the operation data of adjacent equipment to form operation-related data; perform change statistics on the operation-related data to form related change information; S300: Based on the change information of each correlation, perform consistency judgment on the corresponding operational data; when the change trend of the corresponding operational data is inconsistent, generate abnormal correlation information; S400: Determine the operating status of each device based on anomaly correlation information and flow path; generate the status assessment results of the carbon dioxide energy storage system based on the operating status.

[0020] In step S100, for the compression, heat exchange, storage, and energy release processes involved in the operation of the carbon dioxide energy storage system, data on the operating status of the equipment in each operating stage is first collected to obtain multi-dimensional operating data that reflects the flow state of carbon dioxide and the working status of the equipment. The carbon dioxide energy storage system includes compression equipment, heat exchange equipment, storage equipment, and energy release equipment. The compression equipment is used to compress and increase the pressure of low-pressure carbon dioxide, the heat exchange equipment is used to realize the heat exchange between carbon dioxide and the heat exchange medium, the storage equipment is used to store carbon dioxide under high or low pressure, and the energy release equipment is used to output energy by utilizing the expansion process of high-pressure carbon dioxide.

[0021] Specifically, during the operation of the carbon dioxide energy storage system, data acquisition devices installed at different detection locations on the compression equipment acquire compression operation data. This data reflects the state changes of carbon dioxide during compression, including inlet pressure data, outlet pressure data, operating power data, and carbon dioxide flow rate data. Specifically, the inlet pressure data characterizes the carbon dioxide pressure state before entering the compression equipment, the outlet pressure data characterizes the pressure increase after compression, the compression power data reflects the energy consumption of the compression equipment, and the carbon dioxide flow rate data reflects the flow of the working fluid through the compression equipment per unit time. These data allow us to obtain information on pressure changes, energy input, and working fluid transport status during the compression process.

[0022] Heat exchange operation data is collected by detection devices installed at the inlet and outlet sides of the heat exchange equipment and on the heat exchange medium pipeline. This data includes inlet temperature data, outlet temperature data, and heat exchange medium operation data. Specifically, the inlet temperature data indicates the temperature of the carbon dioxide entering the heat exchange equipment, the outlet temperature data indicates the temperature of the carbon dioxide after heat exchange, and the heat exchange medium operation data indicates the flow state of the medium participating in the heat exchange process and changes in heat exchange capacity. By collecting this data, the temperature changes of carbon dioxide during the heat exchange process can be obtained, providing a data foundation for subsequent analysis of the heat exchange process's operational status.

[0023] By installing pressure, temperature, and capacity detection units inside the storage device, the operating status during the storage process is collected, forming storage operation data. This data includes internal pressure data, internal temperature data, and storage capacity data. Specifically, the internal pressure data reflects changes in carbon dioxide pressure within the storage device, the internal temperature data reflects changes in the thermal state of carbon dioxide during storage, and the storage capacity data indicates the current amount of carbon dioxide stored within the device. By continuously collecting this data, the trend of the storage device's status changes at different operating stages can be obtained.

[0024] Data acquisition devices installed at the inlet, outlet, and output ends of the energy release device are used to acquire energy release operation data. This data includes inlet pressure data, outlet pressure data, and output power data. Specifically, the inlet pressure data reflects the high-pressure carbon dioxide entering the device, the outlet pressure data reflects the pressure changes after expansion and energy release, and the output power data reflects the current energy output capacity of the device. By acquiring this data, real-time monitoring of the energy release process's operational status can be achieved.

[0025] After completing the collection of the aforementioned operational data, due to the continuous carbon dioxide flow relationship between the compression equipment, heat exchange equipment, storage equipment, and energy release equipment, the data collection time of different equipment may differ. For example, pressure sensors, temperature sensors, and flow sensors have different sampling periods, which may cause time offsets in the data acquired at the same moment. Therefore, this embodiment further synchronizes the operational data to ensure that the operational data of different equipment can be correlated and analyzed under the same time reference.

[0026] Specifically, the first step is to obtain the data acquisition time information corresponding to each operational data point. This data acquisition time information includes at least the data acquisition time, acquisition period, and the device identifier corresponding to the data. Each data acquisition device generates a timestamp corresponding to the operational data while acquiring it, and then associates and stores the timestamp with the corresponding operational data. For example, the inlet pressure data acquired by the compression device corresponds to timestamp T1, the inlet temperature data acquired by the heat exchange device corresponds to timestamp T2, and the internal pressure data acquired by the storage device corresponds to timestamp T3. The time relationship between the operational data of different devices is determined using this time information.

[0027] Based on the data acquisition time information of each operational data point, time matching is performed on the operational data acquired by different devices. Specifically, a data synchronization time range is preset, and data with time differences within the synchronization time range are grouped into the same time data group according to the timestamps of each operational data point. For example, using a certain time node as the base time, the operational data acquired by the compression equipment, heat exchange equipment, storage equipment, and energy release equipment are matched within a preset time window, so that the data of different devices in the same operational stage can be correlated.

[0028] After time matching is completed, the operational data of each device within the same time range are combined to form an operational dataset. The operational dataset includes compression operational data, heat exchange operational data, storage operational data, and energy release operational data corresponding to the same time range, and a data index relationship is established through device identifiers, data types, and time identifiers. For example, within a certain synchronization time window, the outlet pressure data of the compression device, the inlet temperature data of the heat exchange device, the internal pressure data of the storage device, and the inlet pressure data of the energy release device are combined to form a system operational data set for the corresponding time period.

[0029] In step S200, after completing the acquisition and synchronization of operational data, a correlation analysis is further performed on the operational data between different devices based on the actual flow path of the carbon dioxide working fluid within the carbon dioxide energy storage system. Since the compression, heat exchange, storage, and energy release devices in the carbon dioxide energy storage system are connected by pipelines to form a continuous working fluid transfer process, the output state of the upstream device directly affects the input state of the downstream device. Therefore, this embodiment establishes a data correspondence between devices to analyze the coordinated operation status of the devices.

[0030] Specifically, the upstream and downstream relationships between the various devices are first determined based on the flow direction of carbon dioxide in the energy storage system. The flow direction refers to the direction in which carbon dioxide flows from one device to another during system operation. For example, in the energy storage stage, carbon dioxide first enters the compression device for compression and pressurization. The high-pressure carbon dioxide after compression then enters the heat exchange device for heat exchange, and subsequently enters the storage device for storage. In the energy release stage, the high-pressure carbon dioxide in the storage device is output to the energy release device, where it releases energy through an expansion process. Therefore, based on the above process flow, the compression device can be determined as the upstream device of the heat exchange device, the heat exchange device as the upstream device of the storage device, and the storage device as the upstream device of the energy release device.

[0031] Based on the established upstream and downstream relationships between equipment, the operational data of different devices in the operational dataset are filtered and matched. Specifically, the operational data of the output side of upstream equipment is matched with the operational data of the input side of downstream equipment to establish the correspondence between devices reflecting the carbon dioxide transfer process. For example, the outlet pressure and flow data of the compressor are matched with the inlet temperature and pressure data of the heat exchanger; the outlet operational data of the heat exchanger is matched with the internal pressure and temperature data of the storage device; and the output status data of the storage device is matched with the inlet pressure data of the energy release device.

[0032] The matching process considers not only the physical connections between devices but also the time and data types of the operational data. Since there is a delay in the transmission of carbon dioxide between devices, the data transmission time range is determined based on the pipeline length, working fluid velocity, and historical operating status. Within this time range, operational data showing correlation between upstream and downstream devices is searched. For example, when the outlet pressure of the compressor changes, the inlet pressure of the heat exchanger is monitored within a preset transmission time range to detect a corresponding change, thereby establishing a data correlation between the outlet state of the compressor and the inlet state of the heat exchanger.

[0033] After matching, the operational data of adjacent devices are associated and stored to form operational association data. Operational association data includes associated device identifiers, upstream device operational data, downstream device operational data, corresponding time information, and flow relationship information between devices. Specifically, device identifiers identify the specific devices participating in the association analysis, operational data indicates the current operating status of the devices, time information ensures that the associated data are at the same operational stage, and flow relationship information indicates the direction of carbon dioxide transfer between devices.

[0034] For example, the operational correlation data between the compression equipment and the heat exchange equipment includes changes in the outlet pressure and flow rate of the compression equipment, as well as changes in the inlet pressure and temperature of the heat exchange equipment. Similarly, the operational correlation data between the storage equipment and the energy release equipment includes changes in the internal pressure and storage capacity of the storage equipment, as well as changes in the inlet pressure and output power of the energy release equipment. Through this method, the originally independent equipment operational data is transformed into correlated data with a transitive relationship.

[0035] After generating operational correlation data, statistical analysis is performed on this data to examine the dynamic relationships between the operational data of adjacent devices. Specifically, the changes in the corresponding operational data of adjacent devices within the operational correlation data over a continuous time period are first obtained. These changes represent the degree of variation in device operating parameters at different points in time. For example, when pressure data changes from P1 to P2, the pressure change is obtained; when temperature data changes from W1 to W2, the temperature change is obtained; and when flow data changes from F1 to F2, the flow change is obtained.

[0036] Based on the changes in the acquired operational data, the direction of change for each data point is statistically analyzed. The direction of change includes upward, downward, and stable changes. For example, when the outlet pressure of the compressor continuously increases, the operational data is marked as an upward trend; when the internal pressure of the storage device increases with the increase of carbon dioxide input, the corresponding data is marked as a synchronous upward trend; when a parameter of a certain device does not change significantly, it is marked as a stable trend. By statistically analyzing the direction of change, it is possible to determine whether there is a data change relationship between adjacent devices that conforms to the carbon dioxide flow pattern.

[0037] The magnitude of changes in operational data is statistically analyzed. Specifically, the magnitude of change for each operational parameter is determined based on the degree of change in data within a continuous sampling period. For example, for pressure data, the increase in pressure per unit time is statistically analyzed; for temperature data, the range of temperature rise and fall per unit time is statistically analyzed; and for flow rate data, the percentage change in flow rate per unit time is statistically analyzed. By statistically analyzing the magnitude of changes, it is possible to further determine whether the data response levels between different devices are matched.

[0038] The timing of changes in operational data is statistically analyzed. Since the transfer of carbon dioxide from upstream to downstream equipment takes time, data changes from different devices typically exhibit a temporal relationship. This embodiment records the time points when the operational data of the upstream equipment changes and the corresponding time points when the operational data of the downstream equipment changes, obtaining the time interval between the two, and using this time interval as a dynamic response characteristic between the devices.

[0039] Based on the statistical results of the aforementioned changes in direction, magnitude, and timing, correlation change information is generated. This correlation change information describes the correlation between changes in the operating data of adjacent devices, including the associated device identifier, corresponding operating parameters, change direction information, change magnitude information, and change timing information. For example, if the outlet pressure of the compressor increases and the inlet pressure of the heat exchanger increases synchronously within the corresponding time range, then normal correlation change information is generated; if the outlet pressure of the compressor changes significantly, but the inlet pressure of the heat exchanger does not change accordingly, then abnormal trend correlation change information is generated for further assessment.

[0040] In step S300, after completing the construction of operational correlation data and the generation of correlation change information, a consistency judgment is further performed on the operational data of adjacent devices with corresponding relationships to determine whether the data changes between devices conform to the normal transfer law of the carbon dioxide energy storage system. Since there is a continuous mass transfer relationship between devices in the carbon dioxide energy storage system, after the operating status of upstream devices changes, the corresponding operating parameters of downstream devices should change within a certain time range. Therefore, by judging the consistency of the trend of operational data changes between adjacent devices, potential operational anomalies between devices can be identified.

[0041] Specifically, the first step is to obtain the preset correlation and change relationships corresponding to the operating data between adjacent devices. These preset correlation and change relationships describe the changing patterns of operating parameters between different devices under normal operating conditions caused by the flow of carbon dioxide. These preset correlation and change relationships can be established in advance based on the structural parameters of the carbon dioxide energy storage system, device connection relationships, historical operating data, and parameter change patterns under normal operating conditions.

[0042] For example, between a compression device and a heat exchange device, since the high-pressure carbon dioxide output from the compression device directly enters the heat exchange device, when the outlet pressure of the compression device increases, the inlet pressure of the heat exchange device should increase accordingly within a preset transmission time range. At this time, a positive correlation relationship of pressure parameter changes is formed between the two. When the outlet flow rate of the compression device increases, the carbon dioxide flow rate on the inlet side of the heat exchange device should also increase accordingly. At this time, a synchronous change relationship of flow parameter changes is formed.

[0043] Based on the operating characteristics of carbon dioxide entering the storage device after heat exchange, a correlation relationship is established between the outlet state of the heat exchange device and the internal state of the storage device. For example, when the outlet temperature of the heat exchange device decreases, the internal temperature of the storage device should change accordingly within a certain time range; when the flow rate of carbon dioxide entering the storage device increases, the internal pressure or storage capacity of the storage device should show a corresponding trend.

[0044] A correlation is established between the storage and energy release devices based on the carbon dioxide release path during the energy release process. For example, when the output pressure of the storage device decreases, the inlet pressure of the energy release device should decrease synchronously; when the output flow rate of the storage device increases, the output power of the energy release device should change accordingly. This method establishes the relationship between the normal operation data changes of different devices, providing a basis for subsequent consistency assessments.

[0045] The generated associated change information is matched with the corresponding preset associated change relationships. Specifically, for each set of operational associated data, the adjacent device identifier, device operating parameters, change direction, change magnitude, and change time information contained therein are extracted and compared with the pre-established corresponding associated change relationships.

[0046] During the matching process, the first step is to determine whether the direction of change in the operating data between adjacent devices is consistent. For example, when the outlet pressure of the upstream device increases, according to the preset correlation relationship, the inlet pressure of the downstream device should also be increasing. If the inlet pressure of the downstream device also increases, the direction of change is determined to satisfy the correlation relationship. If the inlet pressure of the downstream device remains unchanged or shows a downward trend, the direction of change is determined to not satisfy the correlation relationship.

[0047] The system matches and judges the magnitude of changes in the operating data of adjacent equipment. Under normal operating conditions, changes in upstream equipment parameters will cause changes in downstream equipment parameters within a certain range. Therefore, by comparing the actual magnitude of change with the preset range of change, it is determined whether the data response between the equipment meets expectations. For example, if the outlet pressure of the compressor equipment changes significantly, while the inlet pressure of the heat exchanger equipment changes only slightly, exceeding the normal response range, then the magnitude of change between the two is considered not to meet the correlation relationship.

[0048] The system matches and judges the time changes in the operating data of adjacent devices. Since the transfer of carbon dioxide between devices takes a certain amount of time, the preset correlation relationship includes the response time range between devices. When the change time of the downstream device's operating data is within the preset response time range, the time correlation relationship is considered to be satisfied; when the downstream device does not change within the corresponding time range, or the change time deviates significantly from the preset range, the time correlation relationship is considered abnormal.

[0049] When the associated change information meets the preset associated change relationship, it is determined that the changing trends of the operating data between the corresponding devices are consistent, and the associated data group is marked as a normal associated state. When the associated change information does not meet the preset associated change relationship, it is determined that the changing trends of the corresponding operating data are inconsistent, and abnormal associated information is further generated.

[0050] Specifically, after determining that the operational data trends are inconsistent, the first step is to identify the equipment identifiers of adjacent devices where the anomaly occurred. These equipment identifiers uniquely identify the devices involved in the anomaly correlation determination, such as compressor equipment numbers, heat exchanger equipment numbers, storage equipment numbers, and energy release equipment numbers. Simultaneously, the data type of the anomaly is determined based on the operational correlation data, including pressure data, temperature data, flow data, power data, and storage capacity data.

[0051] Anomaly correlation information is generated based on the identified device identifier and abnormal operation data. This information includes the abnormal device identifier, associated device identifiers, abnormal operation data type, data change trend, anomaly occurrence time, and a description of the anomaly correlation relationship. Specifically, the abnormal device identifier determines the location of the anomaly, the abnormal operation data type identifies the parameters involved, and the change trend information indicates the deviation between the actual changes and the preset change relationship.

[0052] For example, when it is detected that the outlet pressure of the compression device is continuously rising while the inlet pressure of the heat exchange device is not rising synchronously according to the preset time relationship, it is determined that there is an abnormal correlation between the compression device and the heat exchange device, and abnormal correlation information including the compression device identifier, the heat exchange device identifier, the outlet pressure data, the inlet pressure data, and the inconsistent pressure change trend information is generated.

[0053] For multiple anomaly correlation information during continuous operation, it can also be recorded according to the direction of carbon dioxide flow to form anomaly propagation path. For example, if an anomaly first occurs between the compression equipment and the heat exchange equipment, and then an anomaly occurs between the heat exchange equipment and the storage equipment, the sequential relationship between the above anomaly correlation information is recorded to provide a basis for subsequent analysis of the scope of anomaly impact and equipment operating status.

[0054] In step S400, after the abnormal correlation information is generated, the operating status of each device is analyzed by combining the abnormal correlation information and the actual flow path of carbon dioxide in the energy storage system, so as to determine the impact relationship and propagation range of the abnormality between the devices, and generate the overall status assessment result of the carbon dioxide energy storage system based on the operating status of the devices.

[0055] Specifically, the first step is to obtain the generated anomaly association information. This information includes the device identifiers involved in the anomaly association, the data types of operations affected by the anomaly, the anomaly trend, the time of occurrence of the anomaly, and the association information between the corresponding devices. Since anomalies in a carbon dioxide energy storage system typically affect devices along the direction of carbon dioxide flow, this embodiment further sorts the anomaly association information according to the flow direction of carbon dioxide along the flow path.

[0056] Specifically, based on the equipment connection relationships of the carbon dioxide energy storage system, the sequential positions of each device in the flow path are determined, and abnormal correlation information is arranged according to the flow direction of carbon dioxide from the input end to the output end. For example, in the energy storage stage, carbon dioxide passes through the compression device, heat exchange device, and storage device in sequence. When abnormal correlation information is detected between the compression device and the heat exchange device, and also between the heat exchange device and the storage device, the abnormal correlation information between the compression device and the heat exchange device is arranged first, and the abnormal correlation information between the heat exchange device and the storage device is arranged second, according to the flow direction of carbon dioxide.

[0057] By sorting the anomaly association information, the location of the anomaly and the potential propagation direction of its impact can be determined. Specifically, the devices involved in the anomaly association are identified based on the device identifiers in the anomaly association information. For example, if the anomaly association information contains both a compressor device identifier and a heat exchanger device identifier, then an anomaly association is determined between the compressor device and the heat exchanger device; if the anomaly association information contains both a storage device identifier and an energy release device identifier, then an anomaly association is determined between the storage device and the energy release device.

[0058] Based on the equipment relationships and carbon dioxide flow path in the anomaly correlation information, the anomaly propagation range is determined. The anomaly propagation range indicates the number of devices that may be affected by the current anomaly correlation and the direction of impact. Specifically, based on the location of the device where the anomaly occurred in the flow path, an extended analysis is performed on downstream devices in the direction of carbon dioxide flow to determine whether there are continuous anomaly correlations for subsequent devices.

[0059] For example, if the outlet pressure of the compressor changes abnormally, but the inlet pressure of the heat exchanger remains within a normal range, the abnormality is mainly located in a local area of ​​the compressor and the range of abnormality propagation is small. If the outlet pressure of the compressor is abnormal, and the inlet status of the heat exchanger and the internal pressure of the storage equipment also show abnormal correlation changes, the abnormality is propagated along the direction of carbon dioxide flow and the range of abnormality propagation is expanded.

[0060] Based on the determined scope of anomaly propagation, the operating status of each device is analyzed. Specifically, based on whether a device is within the scope of anomaly association, its operating status is divided into normal state, associated anomaly state, and affected state. A normal state indicates that the changes in the device's own operating data and those of adjacent devices all satisfy the preset association relationship; an associated anomaly state indicates that there is an inconsistent trend in the operating data of the corresponding device compared to adjacent devices; and an affected state indicates that the device itself did not directly generate an anomaly, but its operating status is affected due to anomalies in upstream devices.

[0061] For example, if the outlet pressure of the compressor is abnormal and the inlet pressure of the heat exchanger does not change accordingly, the compressor and the heat exchanger will be marked as associated abnormal states; if the storage device's input carbon dioxide state changes due to the abnormality of the heat exchanger, the storage device will be marked as affected.

[0062] Based on determining the operating status of each device, the collaborative operating status between devices is further determined. The collaborative operating status describes whether adjacent devices can maintain a normal data response relationship according to the carbon dioxide flow process. Specifically, when the direction, magnitude, and time of change in the operating data between adjacent devices all meet the preset correlation relationship, the devices are determined to be in a normal collaborative operating state; when there is continuous abnormal correlation information between adjacent devices, the devices are determined to be in an abnormal collaborative operating state.

[0063] For example, under normal operating conditions, if the inlet pressure of the heat exchanger changes within a preset time range after the outlet pressure of the compressor changes, it is considered that the compressor and the heat exchanger are operating normally in coordination. If the inlet pressure of the heat exchanger does not change for a long time after the outlet pressure of the compressor changes, and this further leads to abnormal internal conditions of the storage device, it is considered that there is an abnormality in the coordinated operation of the compressor, the heat exchanger, and the storage device.

[0064] Based on the operating status of each device and the coordinated operating status between devices, an overall status assessment result of the carbon dioxide energy storage system is generated. Specifically, the operating status of each device is first comprehensively analyzed to determine whether there are any abnormal correlations within the system and whether these abnormal correlations are continuously distributed along the carbon dioxide flow path.

[0065] When there is no abnormal correlation information between adjacent devices, it means that the changes in the operating data during the transfer of carbon dioxide between devices are normal, and the devices can maintain effective collaborative operation. At this time, it is determined that the carbon dioxide energy storage system is in normal operation and an assessment result of normal operation status is generated.

[0066] When there is abnormal association information between adjacent devices, but the abnormal association information only exists in the connection relationship of a single device and does not continue to expand along the direction of carbon dioxide flow, the system is determined to be in a local abnormal operating state, and the corresponding abnormal device identifier and abnormal association location are output to prompt maintenance personnel to check the relevant equipment.

[0067] When there is continuous abnormal correlation information between adjacent devices, such as an abnormal correlation between the compression device and the heat exchange device, and an abnormal correlation between the heat exchange device and the storage device, it indicates that the abnormality has spread along the carbon dioxide flow path. At this time, it is determined that the carbon dioxide energy storage system is in an abnormal operating state, and the abnormal device identifier, the abnormal propagation range, and the abnormal correlation path are output.

[0068] After generating the status assessment results, to facilitate subsequent operational analysis and fault tracing, this embodiment also marks the operational data of the devices corresponding to the anomaly association information based on the status assessment results. Specifically, anomaly status identifiers are added to the operational dataset, the operational data of devices with anomaly associations are marked, and the time of anomaly occurrence, the type of anomaly, and the information of the associated devices are recorded.

[0069] Anomaly correlation information, equipment identification, and the operating status of each device are linked and stored to form historical status assessment data. This historical status assessment data includes the anomaly occurrence time, location of the anomalous device, anomaly propagation path, changes in equipment operating status, and system status assessment results. By storing this historical status assessment data, it can be used to analyze the long-term operating trends of the carbon dioxide energy storage system, identify patterns of equipment performance degradation, and provide a data foundation for subsequent fault prediction and operational optimization.

[0070] In summary, based on the actual flow path of carbon dioxide in the energy storage system, a correspondence is established between the operational data of adjacent devices to form operational correlation data. This goes beyond judging the status of single device parameters; instead, by analyzing the operational changes during the transfer of carbon dioxide from upstream to downstream devices, a comprehensive analysis of the coordinated operational status between compression, heat exchange, storage, and energy release is achieved, improving the ability to perceive the overall operational status. By statistically analyzing changes in operational correlation data, the direction, magnitude, and timing of changes in the operational data of adjacent devices are obtained, and the correlation information is used to determine whether the corresponding operational data meet preset change relationships. When inconsistent change trends occur between adjacent devices, abnormal correlation information can be generated in a timely manner, thereby identifying hidden faults where individual device parameters do not significantly exceed limits but the operational relationship between devices has already become abnormal, improving the early detection and accuracy of anomalies. Combining abnormal correlation information with the carbon dioxide flow path, the location of the devices involved in the abnormal information and the scope of anomaly propagation are analyzed to determine the operational status of each device and the coordinated operational status between devices. This realizes the transformation from single-device anomaly identification to system-level status assessment and locates the scope of anomaly impact.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for assessing the state of a carbon dioxide energy storage system based on operational data, characterized in that, include: Acquire operational data of a carbon dioxide energy storage system, including compression operational data, heat exchange operational data, storage operational data, and energy release operational data; The aforementioned operational data are then synchronized to form an operational dataset; Based on the flow path of carbon dioxide and the operational dataset, a correspondence is established between the operational data of adjacent devices to form operational association data; The changes in the aforementioned operational data are statistically analyzed to generate related change information; Based on the aforementioned correlation change information, a consistency judgment is made on the operational data that have the corresponding relationship; when the change trends of the operational data that have the corresponding relationship are inconsistent, abnormal correlation information is generated. Based on the anomaly correlation information and the flow path, the operating status of each device is determined; based on the operating status, a status assessment result of the carbon dioxide energy storage system is generated.

2. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 1, characterized in that, The compression operation data includes inlet pressure data, outlet pressure data, compression power data, and carbon dioxide flow rate data of the compression equipment; the heat exchange operation data includes inlet temperature data, outlet temperature data, and heat exchange medium operation data of the heat exchange equipment; the storage operation data includes internal pressure data, internal temperature data, and storage capacity data of the storage equipment; and the energy release operation data includes inlet pressure data, outlet pressure data, and output power data of the energy release equipment.

3. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 2, characterized in that, When synchronizing the aforementioned operational data, the process includes: Obtain the data acquisition time information corresponding to each of the aforementioned operational data; perform time matching on the operational data acquired by different devices based on the data acquisition time information; combine the operational data of each device within the same time range to form the operational dataset.

4. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 3, characterized in that, When generating runtime-related data, it includes: Based on the flow direction of carbon dioxide, the upstream and downstream relationships between various devices are determined; the operating data on the output side of the upstream device is matched with the operating data on the input side of the downstream device to establish the correspondence between adjacent devices and form the operating association data.

5. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 4, characterized in that, When generating related change information, it includes: Obtain the change in the operating data of adjacent devices in the operating association data; statistically analyze the change direction, change magnitude, and change time of the operating data respectively; and generate the association change information of the operating data association change situation based on the statistical results.

6. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 5, characterized in that, When performing a consistency check on the runtime data that have the aforementioned correspondence, the following steps are included: Obtain a preset correlation change relationship of the operating data between adjacent devices; match the correlation change information with the preset correlation change relationship; when the correlation change information does not satisfy the correlation change relationship, determine that the operating data change trends of the corresponding relationship are inconsistent.

7. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 6, characterized in that, When generating anomaly association information, the following are included: Identify the device identifiers and operational data of adjacent devices with inconsistent trends of change; generate the abnormal association information based on the device identifiers and operational data.

8. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 7, characterized in that, Determining the operating status of each device includes: Based on the flow direction of carbon dioxide along the flow path, the abnormal association information is sorted; the device identifier and abnormal propagation range of the abnormal association information are determined; based on the abnormal propagation range, the operating status of each device and the cooperative operating status between devices are determined.

9. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 8, characterized in that, When generating state assessment results, the following are included: Based on the operating status of each device, the overall operating status of the carbon dioxide energy storage system is determined; when there is no abnormal correlation information between adjacent devices, the carbon dioxide energy storage system is determined to be in normal operating status; when there is continuous abnormal correlation information between adjacent devices, the carbon dioxide energy storage system is determined to be in abnormal operating status, and an abnormal device identifier is output.

10. The method for assessing the state of a carbon dioxide energy storage system based on operational data according to claim 9, characterized in that, Also includes: Based on the status assessment results, the operating data of the device with the abnormal association information is marked; The abnormal association information, the device identifier, and the operating status of each device are associated and stored to form historical status assessment data.

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