A method and system for storing data at measurement points in an energy storage power station

Abstract

This invention relates to the field of power energy storage system technology, specifically to a data storage method and system for measuring points in an energy storage power station. The method includes: S1: Dividing measuring points into multiple association groups based on electrical and physical correlations, and storing the measuring point data of the same association group in a time-series database to the same virtual node; S2: Selecting the numerical sequence of one measuring point as a baseline column, calculating and storing the differences between the remaining measuring points and the baseline column; S3: Dividing the time-series data of the association group into several cycle segments according to the charge / discharge cycle boundaries, calculating and storing the aggregate value for each cycle segment; S4: Sorting the cycle segments, retaining the aggregate value and difference data of cycle segments with a sorting number less than or equal to a first preset value; for cycle segments with a sorting number greater than the first preset value, only retaining the cycle summary information and deleting the remaining data. This invention solves the technical problems of low data compression and storage efficiency and unreasonable allocation of storage resources in the prior art.

Description

Technical Field

[0001] This invention relates to the field of power energy storage system technology, and more specifically, to a data storage method and system for measuring points in an energy storage power station. Background Technology

[0002] As the installed capacity of energy storage power stations gradually expands, the time-series measurement data generated by their internal equipment is growing explosively. This data mainly includes key operating parameters such as voltage, current, power, and temperature, and has time-series data characteristics such as strict time sequence, high writing frequency, and query analysis with time range as the main dimension.

[0003] Existing energy storage data storage solutions based on time-series databases have the following shortcomings:

[0004] Firstly, existing technologies employ intra-column compression on a per-measurement-point basis in the data compression stage. This involves independently performing differential encoding on the time-series data of each measurement point, leveraging the smooth numerical changes between adjacent timestamps at the same measurement point to achieve compression. However, this compression method ignores the electrical and physical correlations between measurement points in an energy storage power station. For example, all series-connected cells within the same battery cluster carry the same current, and the voltages of each cell tend to converge under steady-state conditions. Existing single-column independent compression methods do not utilize this redundant information across measurement points, resulting in low data compression and storage efficiency.

[0005] Secondly, existing solutions generally adopt a uniform time strategy for the lifecycle management of time-series data, that is, cleaning all data with the same retention period. The operation of energy storage power stations is based on charge-discharge cycles, and business operations such as battery health status assessment rely on the analysis of historical charge-discharge cycle data. However, existing solutions cannot differentiate the retention of data according to the dimension of charge-discharge cycles, resulting in high-value recent cycle detailed data and low-value long-term cycle data occupying the same amount of storage resources, leading to unreasonable allocation of storage resources.

[0006] In summary, existing technologies suffer from low data compression and storage efficiency and unreasonable allocation of storage resources. Summary of the Invention

[0007] The purpose of this application is to provide a data storage method and system for measuring points in energy storage power stations, which solves the technical problems of low data compression and storage efficiency and unreasonable allocation of storage resources in the prior art.

[0008] To solve the above-mentioned technical problems, the solution adopted in this application is as follows:

[0009] A method for storing data at measurement points in an energy storage power station, characterized by comprising:

[0010] S1: Obtain the electrical and physical correlation between each measuring point in the energy storage power station, divide the measuring points into no less than 2 correlation groups according to the electrical and physical correlation, and store the measuring point data of the same correlation group in the time series database to the same virtual node;

[0011] S2: For time series data of multiple measurement points within the same association group, select the numerical sequence of one measurement point as the reference column, calculate the difference data between the numerical sequences of the remaining measurement points and the reference column at the same timestamp, and use the difference data to replace the original numerical sequences of the remaining measurement points for storage.

[0012] S3: Identify the cycle boundary based on the charge / discharge cycle status bit, divide the time series data of the associated group into several cycle segments according to the cycle boundary, calculate the aggregate value for each cycle segment according to the preset time granularity and store it;

[0013] S4: Sort the completion time of the loop segments in descending order, and retain the aggregate value and difference data of the loop segments whose sorting number is less than or equal to the first preset value; for the loop segments whose sorting number is greater than the first preset value, only retain the loop summary information of the loop segment, and delete the data in the loop segment other than the loop summary information; repeat S1-S4 to process the measurement point data in real time.

[0014] The cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for this cycle segment;

[0015] In addition to the loop summary information, the data includes aggregated values ​​and difference data within the loop segment.

[0016] Preferably, the specific implementation of S1 includes the following steps:

[0017] S1.1: Obtain the electrical topology data of the energy storage power station. The electrical topology data includes: equipment list, electrical connection relationship of equipment, and secondary control signal relationship;

[0018] S1.2: Analyze the electrical topology data item by item to identify the electrical physical correlation characteristics between measurement points;

[0019] S1.3: Based on the electrical and physical correlation characteristics between the identified measurement points, the measurement points are divided into association groups, and measurement points belonging to the same electrical and physical correlation characteristics are divided into the same association group; each association group contains no less than two measurement points;

[0020] S1.4: Establish location binding relationships between the divided association groups and the storage partition units in the time series database.

[0021] Preferably, the electrical-physical correlations include series voltage correlations, three-phase current symmetry correlations, power conservation correlations, and control-response correlations.

[0022] Preferably, the specific implementation method of S2 includes the following steps:

[0023] S2.1: Obtain the aligned time-series data block of the test points within the same associated group;

[0024] S2.2: In the aligned time series data block, select the numerical sequence of one measurement point in the associated group as the reference column;

[0025] S2.3: For each measurement point in the associated group other than the reference column, calculate the difference sequence between its numerical sequence and the reference column to obtain the difference data matrix;

[0026] S2.4: Perform threshold judgment on each difference in the difference data matrix and update the difference data matrix according to the threshold judgment result;

[0027] S2.5: Write the processed difference data matrix into the time series database in columnar storage mode.

[0028] Preferably, the specific implementation method of S3 includes:

[0029] S3.1: Obtain the charge / discharge cycle status data of the energy storage power station;

[0030] The charge / discharge cycle status bit data includes: charging state, discharging state, and standby state;

[0031] S3.2: Scan and identify the charge and discharge cycle status data to identify the cycle boundaries of the energy storage power station and divide the charge and discharge cycle segments.

[0032] Each charge / discharge cycle segment includes the following attributes: cycle segment number, cycle segment start timestamp, and cycle segment end timestamp;

[0033] S3.3: Divide the time series data of the associated group according to the cycle boundary of the charge-discharge cycle segment, and classify the difference data matrix and the original numerical sequence of the benchmark column belonging to the same time range of the charge-discharge cycle segment into the data set of that charge-discharge cycle segment;

[0034] S3.4: Calculate the aggregated value for the data set of each charge-discharge cycle segment according to the preset time unit. The aggregated value types include second-level aggregated value, minute-level aggregated value, and hour-level aggregated value.

[0035] S3.5: Store the calculated aggregate value into the time series database and maintain the cyclic segment index information.

[0036] Preferably, the aggregated values ​​at the second, minute, and hour levels all include the mean, maximum, minimum, and cumulative values;

[0037] Aggregate value calculations include: second-level calculations, minute-level calculations, and hour-level calculations;

[0038] The second-level aggregated value is obtained by calculating the mean, maximum, minimum and cumulative values ​​of the original values ​​of each measuring point within a 1-second time window.

[0039] The minute-level aggregated value is obtained by calculating the minute-level aggregated value. Specifically, a second-level aggregated value is calculated within a 1-minute time window.

[0040] The hourly aggregate value is obtained by calculating the hourly aggregate value. Specifically, the minute-level aggregate value within the time window is calculated twice, with a time window of 1 hour.

[0041] Preferably, the specific implementation method of S4 includes:

[0042] S4.1: Read the cyclic segment index information from the time series database;

[0043] S4.2: For each associated group, sort all its loop segment index information in descending order according to the loop segment end timestamp to obtain a list of loop segment indexes in descending order;

[0044] S4.3: Perform differential retention on data in different loop segments based on the descending order of the serial number and the first preset value;

[0045] S4.4: After completing the differentiation retention, update the circular segment index information of the associated group.

[0046] Preferably, in step S4.3, the differential retention of data in different loop segments is performed based on the descending order of the sequence number and the first preset value. The specific implementation method includes:

[0047] For cyclic segments with descending order and serial numbers less than or equal to the first preset value, all data of the cyclic segment is retained, including: cyclic summary information, aggregated values ​​of each time unit in the aggregated record, difference data matrix, and original numerical sequence of the benchmark column; among which, the cyclic summary information is extracted from the aggregated values ​​and retained as well.

[0048] For loop segments with indexes greater than the first preset value in descending order, perform the following processing:

[0049] Extract and save the cycle summary information for this cycle segment; the cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for this cycle segment;

[0050] Delete all data in this loop segment except for the loop summary information, including: aggregated values ​​for each time unit, difference data matrix, and original numerical sequence of the baseline column;

[0051] Release the storage space occupied by the deleted data.

[0052] Preferably, the selection method for the benchmark column includes:

[0053] The numerical sequence of the first measurement point within the associated group is selected as the baseline column;

[0054] The measurement point with the smallest variance in the numerical sequence within the associated group is selected as the baseline column;

[0055] Select the measurement point with the highest / lowest device code in the associated group as the baseline column.

[0056] A data storage system for measuring points in an energy storage power station, used to execute the data storage method for measuring points in an energy storage power station as described in claims 1-9, comprising:

[0057] The association grouping module is used to obtain the electrical and physical correlation between various measurement points in the energy storage power station. Based on the electrical and physical correlation, the measurement points are divided into no less than two association groups, and the measurement point data of the same association group are stored in the time series database to the same virtual node.

[0058] The cross-column compression module is used to select the numerical sequence of one of the measurement points as the reference column for time series data of multiple measurement points within the same association group, calculate the difference data between the numerical sequences of the remaining measurement points and the reference column at the same timestamp, and use the difference data to replace the original numerical sequences of the remaining measurement points for storage.

[0059] The cycle aggregation module is used to identify cycle boundaries based on the charge / discharge cycle status bit, divide the time-series data of the associated group into multiple cycle segments according to the cycle boundaries, calculate and store the aggregation value for each cycle segment according to a preset time unit;

[0060] The lifecycle management module is used to sort the completion time of loop segments in descending order, retain the aggregate value and difference data of loop segments whose sorting number is less than or equal to the first preset value; for loop segments whose sorting number is greater than the first preset value, only the loop summary information of the loop segment is retained, and the data in the loop segment other than the loop summary information is deleted.

[0061] The cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for that cycle segment; data other than the cycle summary information includes aggregated values ​​and difference data within that cycle segment.

[0062] The technical solution of this application has at least the following advantages and beneficial effects:

[0063] 1. The technical solution in this invention forms a data storage method for energy storage power station measurement points by following the processing logic of associating and grouping measurement points, compressing and storing them, calculating and processing cyclically aggregated values, and managing the data's differentiated lifecycle.

[0064] This invention obtains the electrical and physical correlation between various measuring points in an energy storage power station, groups measuring points with the same electrical and physical correlation characteristics into the same association group, and performs differential encoding on the measuring point data within the same association group. That is, a baseline column is selected and its original numerical sequence is stored completely, while the data of other measuring points in the association group are only stored as the difference data between the baseline column and the data of the baseline column at the same timestamp. Since the measuring points in the association group have the same electrical and physical correlation characteristics, the range of variation of the difference data is very small. Therefore, the data compression and storage based on the same electrical and physical correlation characteristics is more effective than the traditional method of differential encoding and compression of measuring point data of the same measuring point at different time periods according to its changing trend. The measuring point data of the same measuring point at different time periods will vary greatly with the working state of the energy storage power station. Therefore, the data compression and storage effect of this method is better.

[0065] Based on the above, this invention, based on the business characteristics of energy storage power stations, divides the time-series data within the associated group into multiple cycle segments according to the charge-discharge cycle boundaries of the energy storage power station, calculates the aggregate value of each cycle segment according to multi-level time units, and finally arranges the cycle segments according to their completion time. Differentiated lifecycle management is performed on different cycle segments, that is, different data is stored for different cycle segments. Thus, through this storage method, limited storage resources can be allocated more efficiently under the constraint of limited storage capacity, solving the technical problems of low data compression storage efficiency and unreasonable allocation of storage resources in the prior art. Attached Figure Description

[0066] Figure 1 This is a flowchart of the energy storage power station measurement point data storage method of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] See Figure 1 The present invention provides a method for storing data at measurement points in an energy storage power station, comprising the following steps:

[0069] S1: Obtain the electrical and physical correlation between each measuring point in the energy storage power station, divide the measuring points into no less than 2 correlation groups according to the electrical and physical correlation, and store the measuring point data of the same correlation group in the time series database to the same virtual node;

[0070] S2: For time series data of multiple measurement points within the same association group, select the numerical sequence of one measurement point as the reference column, calculate the difference data between the numerical sequences of the remaining measurement points and the reference column at the same timestamp, and use the difference data to replace the original numerical sequences of the remaining measurement points for storage.

[0071] S3: Identify the cycle boundary based on the charge / discharge cycle status bit, divide the time series data of the associated group into several cycle segments according to the cycle boundary, calculate the aggregate value for each cycle segment according to the preset time granularity and store it;

[0072] S4: Sort the completion time of the loop segments in descending order, and retain the aggregate value and difference data of the loop segments whose sorting number is less than or equal to the first preset value; for the loop segments whose sorting number is greater than the first preset value, only retain the loop summary information of the loop segment, and delete the data in the loop segment other than the loop summary information; repeat S1-S4 to process the measurement point data in real time.

[0073] The cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for this cycle segment;

[0074] In addition to the loop summary information, the data includes aggregated values ​​and difference data within the loop segment.

[0075] In this embodiment, in S1, the electrical and physical correlation between various measuring points in the energy storage power station is obtained. Based on the electrical and physical correlation, the measuring points are divided into no less than two association groups, and the measuring point data of the same association group are stored in the same virtual node in the time series database. The specific implementation method includes the following steps:

[0076] S1.1: Obtain the electrical topology data of the energy storage power station. The electrical topology data includes: equipment list, electrical connection relationship of equipment, and secondary control signal relationship;

[0077] Specifically, the equipment list includes the equipment code, rated power, and access bus number for each PCS; the equipment code, battery stack number, and number of cells connected in series for each battery cluster; and the equipment code and battery cluster number managed by each BMS.

[0078] The electrical connection relationships are as follows: the bus number of the PCS AC connection and the list of other PCS connected in parallel on the bus; the battery stack number of the PCS DC connection; the parallel connection relationship of each battery cluster in the battery stack; and the series connection sequence of the cells in each battery cluster.

[0079] The relationship between secondary control signals is as follows: the target PCS list for BMS protection commands; the target PCS list for EMS scheduling commands.

[0080] S1.2: Analyze the electrical topology data item by item to identify the electrical physical correlation characteristics between measurement points;

[0081] Electrical and physical correlations include series voltage correlations, three-phase current symmetry correlations, power conservation correlations, and control-response correlations.

[0082] Specifically, series voltage correlation refers to the situation where all cells in the same battery cluster are connected in series and the same current flows through them. Under steady state, the voltage of each cell can be considered the same. The voltage measurement points of all cells in the battery cluster are marked as series voltage correlation.

[0083] Three-phase current symmetrical correlation refers to the fact that the A-phase current, B-phase current, and C-phase current of the same PCS have equal amplitudes and phase differences of 120° under normal operating conditions, and there is a symmetrical correlation between the effective values ​​and instantaneous values ​​of the A-phase current, B-phase current, and C-phase current. The three-phase current measurement points of the PCS are then marked as three-phase current symmetrical correlation.

[0084] Power conservation correlation refers to the fact that the sum of the output power of multiple PCS connected in parallel on the same bus is equal to the total power of the bus. The relationship between the power measurement points of each PCS on the bus and the total power measurement point of the bus is marked as power conservation correlation.

[0085] Control-response correlation refers to the change in active power output of the target PCS after the EMS issues a power command; and the restriction of active power output of the target PCS after the BMS issues a protection command. The relationship between the command measurement point and the response measurement point on the same control link is marked as control-response correlation.

[0086] It is worth emphasizing that the above-mentioned electrical and physical characteristics can coexist, and the same measurement point can belong to multiple electrical and physical characteristics.

[0087] S1.3: Based on the electrical and physical correlation characteristics between the identified measurement points, the measurement points are divided into association groups, and measurement points belonging to the same electrical and physical correlation characteristics are divided into the same association group; each association group contains no less than two measurement points;

[0088] Specifically, the voltage measurement points of all cells within the same battery cluster are grouped into a series voltage association group, which is identified as: battery cluster number_voltage group;

[0089] Three-phase current measurement points in the same PCS are grouped into a three-phase current association group, which is identified as: PCS number_current group;

[0090] Each PCS power measurement point on the same busbar and the total power measurement points on that busbar are grouped into a power conservation association group, which is identified as: busbar number_power group;

[0091] Command and response measurement points on the same control link are grouped into a control-response association group, which is identified as: link number_control response group.

[0092] It should be noted that when the same measurement point meets multiple correlation group division conditions at the same time, it is given priority to be classified into the correlation group type that maximizes the data compression gain when the difference data is used to replace the original numerical sequence for storage; among them, the priority from high to low is: series voltage correlation, three-phase current symmetry correlation, power conservation correlation, and control-response correlation.

[0093] S1.4: Establish location binding relationships between the divided association groups and the storage partition units in the time series database;

[0094] Specifically, a super table is created, and the same virtual node identifier is assigned to all measurement points within the same association group, so that the time series data of all measurement points within the same association group are stored in the time series database in the same virtual node; the virtual node is the storage partition unit in the time series database.

[0095] Virtual nodes are units in a time-series database used for logically or physically isolated storage areas.

[0096] For example, in TDengine, time series data for each measurement point can be stored adjacently on the physical storage medium by specifying the same VGROUP parameter when creating a super table.

[0097] Storing data adjacently on the physical storage medium means aligning the time-series data of each measurement point in the association group according to its timestamp and writing it into the same data file or consecutive data blocks of the underlying storage medium, so as to reduce the number of random disk I / Os generated when querying the time-series data of the association group.

[0098] Furthermore, after dividing the associated groups, the associated groups are also dynamically updated in real time. Specifically, when the equipment in the energy storage power station is expanded / reduced, the electrical and physical characteristics are changed, or the measurement points are added or deleted, the changed electrical topology data is read, the changed electrical and physical characteristics between the measurement points are identified based on the changed electrical topology data, and the associated groups of the measurement points are divided and updated.

[0099] For example, when adding a new device or measurement point, the electrical topology data of the new device or measurement point is read to determine whether it constitutes a new electrical and physical related characteristic with the existing device or measurement point. If so, it is assigned to an existing association group; otherwise, a new association group is created.

[0100] In this embodiment, in S2, for time series data of multiple measurement points within the same association group, the numerical sequence of one measurement point is selected as the baseline column. The difference between the numerical sequences of the remaining measurement points and the baseline column at the same timestamp is calculated, and the difference data is used to replace the original numerical sequences of the remaining measurement points for storage. The specific implementation method includes the following steps:

[0101] S2.1: Obtain the aligned time-series data block of the test points within the same associated group;

[0102] Specifically, for each associated group, time-series data of a preset duration or a preset number are accumulated to form an aligned time-series data block. The aligned time-series data block is an aligned time-series data matrix. In this aligned time-series data matrix, each row corresponds to a timestamp, and each column corresponds to the numerical sequence of a measurement point within the associated group. All rows of timestamps are aligned.

[0103] For example, for the series voltage association group identified as BatteryCluster01_Voltage, its aligned timing data matrix contains the voltage value sequence of all 200 cells in the 01 battery cluster, with timestamps from T1 to T1000.

[0104] S2.2: In the aligned time series data block, select the numerical sequence of one measurement point in the associated group as the reference column;

[0105] Furthermore, the methods for selecting benchmark columns include:

[0106] The numerical sequence of the first measurement point within the associated group is selected as the baseline column;

[0107] The measurement point with the smallest variance in the numerical sequence within the associated group is selected as the baseline column;

[0108] Select the measurement point with the highest / lowest device code in the associated group as the baseline column;

[0109] When selecting the baseline column, simply choose one of the baseline column selection methods based on the actual scenario and requirements.

[0110] In this embodiment, the method of selecting the measurement point with the smallest variance in the numerical sequence within the association group as the benchmark column is adopted. The benchmark measurement point is determined in advance by calculating the variance of the historical data sample of the association group; the selected measurement point as the benchmark column is called the benchmark measurement point.

[0111] S2.3: For each measurement point in the associated group other than the baseline column, calculate the difference sequence between its numerical sequence and the baseline column to obtain the difference data matrix, specifically:

[0112] For each timestamp in the numerical sequence of each measurement point, the corresponding value is... Values ​​at the same timestamp as the baseline column Subtract them to get the difference. , ;

[0113] After the difference sequence of all measuring points in the associated group has been calculated, a difference data matrix is ​​formed. In the difference data matrix, the reference column retains the original numerical sequence, and all other columns are the difference sequence with the reference column.

[0114] For example, within the series voltage correlation group, cell 1 is selected as the reference column, and its original voltage value sequence [3.201, 3.202, 3.200, ...] is stored; cell 2 is stored as the difference sequence between cell 1 and cell 1 [-0.002, -0.001, -0.003, ...].

[0115] S2.4: Perform threshold judgment on each difference in the difference data matrix and update the difference data matrix according to the threshold judgment result, specifically:

[0116] like If the difference threshold is set, the stored value of that measurement point at that timestamp will remain the difference. ;

[0117] like If the difference threshold is reached, the stored value of the measurement point at that timestamp will be restored to its original value. ;

[0118] The difference The absolute value of.

[0119] Specifically, if the absolute value of the difference is greater than the preset difference threshold, it indicates that the electrical and physical correlation characteristics between the measurement points within the associated group of that timestamp point have deviated abnormally. In this case, the difference is no longer close to 0, and storing it in the form of the difference may actually increase the encoding length. Therefore, for that timestamp point, the value of the measurement point is restored to its original value. Store;

[0120] The difference threshold can be limited according to the actual application scenario requirements. In this invention, its specific value range is not limited.

[0121] More specifically, after the threshold determination is completed, a marker area is set at the beginning of each row of data in the difference data matrix. The marker area is used to record the difference stored at each measurement point in the row of the difference data matrix at that timestamp. Still the original values ;

[0122] The specific implementation of setting the marker area is as follows: Add a bitmap to the front of each row of data. The length of the bitmap is equal to the number of measurement points in that row excluding the base column. Each bit in the bitmap corresponds to a measurement point according to the order of the measurement points. A bit value of the first value indicates that the corresponding measurement point stores the difference data at that timestamp, and a bit value of the second value indicates that the corresponding measurement point stores the original value at that timestamp.

[0123] The first value is 1, and the second value is 0.

[0124] S2.5: Write the processed difference data matrix into the time series database in columnar storage format;

[0125] Specifically, when performing columnar storage, different compression algorithms are used for the difference sequence and the original numerical sequence. Since the difference sequence has a small range of variation and a concentrated distribution, run-length encoding is used for compression storage. For the original numerical sequence, which has a large range of variation and a scattered distribution, the standard block compression algorithm is used for compression storage.

[0126] In this embodiment, in step S3, the cycle boundary is identified based on the charge / discharge cycle status bit. The time-series data of the associated group is divided into several cycle segments according to the cycle boundary. The aggregate value of each cycle segment is calculated and stored according to a preset time granularity. The specific implementation method includes:

[0127] S3.1: Obtain the charge / discharge cycle status data of the energy storage power station;

[0128] The charge / discharge cycle status bit data includes: charging state, discharging state, and standby state;

[0129] Specifically, charge / discharge cycle status bit data is extracted from the real-time data streams of the battery management system (BMS) and energy management system (EMS); the charge / discharge cycle status bit data is a marker of the current operating status of the energy storage power station.

[0130] More specifically, the method for obtaining the charge / discharge cycle status bit data is as follows: the charge / discharge status field in the device status message sent by BMS and EMS is parsed into the charge / discharge cycle status bit value, and the charge / discharge cycle status bit value and its corresponding timestamp are written into the charge / discharge cycle status bit data.

[0131] For example, if the BMS sends a status message at 10:00:00 on a certain day with the charge / discharge status field value of "1", then a record [timestamp: 10:00:00, status bit: charging state] will be recorded in the charge / discharge cycle status bit data; if the charge / discharge status field changes to "0" at 10:30:00, then a record [timestamp: 10:30:00, status bit: standby state] will be recorded in the charge / discharge cycle status bit data. The charge / discharge cycle status bit data is updated dynamically in real time.

[0132] S3.2: Scan and identify the charge and discharge cycle status data to identify the cycle boundaries of the energy storage power station and divide the charge and discharge cycle segments.

[0133] Specifically, when the charging / discharging state changes from charging to standby or from discharging to standby within the energy storage power station, the moment of this state change is marked as the end boundary of a cycle and also the starting boundary of the next cycle. The time interval between two adjacent cycle boundaries constitutes a complete charging / discharging cycle segment.

[0134] Each charge / discharge cycle segment includes the following attributes: cycle segment number, cycle segment start timestamp, and cycle segment end timestamp.

[0135] The loop segment numbers are assigned in ascending order based on the loop completion time.

[0136] For example, the charge / discharge cycle status data for a certain morning is shown in the table below:

[0137] Timestamp status bit 08:00:00 charging state 10:00:00 standby mode 10:30:00 Discharge state 12:00:00 standby mode 13:00:00 charging state

[0138] The results of scanning and identifying the cycle boundaries based on the above charge / discharge cycle status bit data are as follows:

[0139] At 10:00:00, the status bit changes from charging to standby, marking the cycle boundary and dividing cycle segment 1, i.e., 08:00:00-10:00:00, which is the charging segment; at 12:00:00, the status bit changes from discharging to standby, dividing cycle segment 2, i.e., 10:30:00-12:00:00, which is the discharging segment.

[0140] S3.3: Divide the time series data of the associated group according to the cycle boundary of the charge-discharge cycle segment, and classify the difference data matrix and the original numerical sequence of the benchmark column belonging to the same time range of the charge-discharge cycle segment into the data set of that charge-discharge cycle segment;

[0141] Specifically, when segmenting the time series data of the associated group, the start and end timestamps of the charge-discharge cycle segment are used as search conditions to extract all the difference data and the original numerical sequence of the benchmark column of the associated group within the time range of all the time series data in the associated group. All the time series data of an associated group are arranged in the order of the cycle segment number and do not overlap with each other.

[0142] S3.4: Calculate the aggregated value for the data set of each charge-discharge cycle segment according to the preset time unit. The aggregated value types include second-level aggregated value, minute-level aggregated value, and hour-level aggregated value.

[0143] The aggregated values ​​at the second, minute, and hour levels all include the mean, maximum, minimum, and cumulative values.

[0144] Specifically, before calculating the aggregate value, the difference data is added to the original value of the baseline column corresponding to the timestamp to restore the original value of each measurement point, and then the aggregate value is calculated.

[0145] The aggregate value calculation includes: second-level calculation, minute-level calculation, and hour-level calculation;

[0146] The second-level aggregated value is obtained by calculating the mean, maximum, minimum and cumulative values ​​of the original values ​​of each measuring point within a 1-second time window.

[0147] The minute-level aggregated value is obtained by calculating the minute-level aggregated value. Specifically, a second-level aggregated value is calculated within a 1-minute time window.

[0148] The hourly aggregate value is obtained by calculating the hourly aggregate value. Specifically, the minute-level aggregate value within the time window is calculated twice, with a time window of 1 hour.

[0149] S3.5: Store the calculated aggregate value into the time series database and maintain the cyclic segment index information;

[0150] Specifically, each charge / discharge cycle segment corresponds to an aggregate record, which includes: associated group identifier, cycle segment number, cycle segment start timestamp, cycle segment end timestamp, and an array of aggregate values ​​for each time unit;

[0151] The cycle segment index information is a set of data used to quickly retrieve aggregated records of charge-discharge cycle segments, including: cycle segment number, associated group identifier, cycle segment start timestamp, cycle segment end timestamp, and the storage location reference of the aggregated record in the time-series data; the cycle segment index information is used for time sorting and lifecycle management in step S4 of the cycle segment completion process.

[0152] In this embodiment, in S4, the completion times of the loop segments are sorted in descending order, and the aggregate value and difference data of the loop segments with sorting numbers less than or equal to the first preset value are retained; for loop segments with sorting numbers greater than the first preset value, only the loop summary information of the loop segment is retained, and the data in the loop segment other than the loop summary information is deleted. The specific implementation includes the following steps:

[0153] S4.1: Read the cyclic segment index information from the time series database;

[0154] S4.2: For each associated group, sort all its loop segment index information in descending order according to the loop segment end timestamp to obtain a list of loop segment indexes in descending order;

[0155] Specifically, after sorting in descending order, the cycle segment with the sequence number 1 is the cycle segment that completes charging and discharging the latest.

[0156] S4.3: Perform differential retention on data in different loop segments based on the descending order of the serial number and the first preset value;

[0157] Specifically, the first preset value is determined based on the amount of historical cycle data required for the battery health status assessment of the energy storage power station. The battery health status assessment relies on trend analysis of detailed operating data from several recent complete charge-discharge cycles, and the value of the first preset value is not less than the minimum number of historical cycles required by the battery health status assessment algorithm.

[0158] For example, the battery health status assessment algorithm uses a sliding window regression model, which requires the input of complete data from at least the most recent 100 charge-discharge cycles. Therefore, the first preset value is set to 100.

[0159] This first preset setting ensures that the complete historical data required for battery health assessment is not erased, while only retaining cycle summary information for any excess data to save storage space.

[0160] S4.4: After completing the differentiation retention, update the circular segment index information of the associated group;

[0161] Specifically, for loop segments that retain only loop summary information, the aggregate record storage location reference in their loop segment index list is updated to the storage location reference of the loop summary information, and the loop segment is marked as "summary retention status"; for loop segments that retain all data, their loop segment index list remains unchanged.

[0162] Furthermore, in S4.3, differential retention is performed on the data of different loop segments based on the descending order of the sequence number and the first preset value. The specific implementation method includes:

[0163] For cyclic segments with descending order and serial numbers less than or equal to the first preset value, all data of the cyclic segment is retained, including: cyclic summary information, aggregated values ​​of each time unit in the aggregated record, difference data matrix, and original numerical sequence of the benchmark column; among which, the cyclic summary information is extracted from the aggregated values ​​and retained as well.

[0164] For loop segments with indexes greater than the first preset value in descending order, perform the following processing:

[0165] Extract and save the cycle summary information for this cycle segment; the cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for this cycle segment;

[0166] Delete all data in this loop segment except for the loop summary information, including: aggregated values ​​for each time unit, difference data matrix, and original numerical sequence of the baseline column;

[0167] Release the storage space occupied by the deleted data.

[0168] The above-mentioned loop summary information can be extracted or calculated from the aggregated record of the loop segment. The specific calculation formulas for each parameter in the above-mentioned loop summary information are conventional technical means in this field and will not be elaborated here.

[0169] Another aspect of the present invention provides a data storage system for measuring points in an energy storage power station, used to execute the aforementioned method for data storage of measuring points in an energy storage power station, comprising:

[0170] The association grouping module is used to obtain the electrical and physical correlation between various measurement points in the energy storage power station. Based on the electrical and physical correlation, the measurement points are divided into no less than two association groups, and the measurement point data of the same association group are stored in the time series database to the same virtual node.

[0171] The cross-column compression module is used to select the numerical sequence of one of the measurement points as the reference column for time series data of multiple measurement points within the same association group, calculate the difference data between the numerical sequences of the remaining measurement points and the reference column at the same timestamp, and use the difference data to replace the original numerical sequences of the remaining measurement points for storage.

[0172] The cycle aggregation module is used to identify cycle boundaries based on the charge / discharge cycle status bit, divide the time-series data of the associated group into multiple cycle segments according to the cycle boundaries, calculate and store the aggregation value for each cycle segment according to a preset time unit;

[0173] The lifecycle management module is used to sort the completion time of loop segments in descending order, retain the aggregate value and difference data of loop segments whose sorting number is less than or equal to the first preset value; for loop segments whose sorting number is greater than the first preset value, only the loop summary information of the loop segment is retained, and the data in the loop segment other than the loop summary information is deleted.

[0174] The cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for that cycle segment; data other than the cycle summary information includes aggregated values ​​and difference data within that cycle segment.

[0175] The various embodiments of the present invention have now been described in detail. To avoid obscuring the concept of the invention, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions of this invention based on the above description, and the scope of the invention is defined by the appended claims.

Claims

1. A method for storing data at measurement points in an energy storage power station, characterized in that, include: S1: Obtain the electrical and physical correlation between each measuring point in the energy storage power station, divide the measuring points into no less than 2 correlation groups according to the electrical and physical correlation, and store the measuring point data of the same correlation group in the time series database to the same virtual node; S2: For time series data of multiple measurement points within the same association group, select the numerical sequence of one measurement point as the reference column, calculate the difference data between the numerical sequences of the remaining measurement points and the reference column at the same timestamp, and use the difference data to replace the original numerical sequences of the remaining measurement points for storage. S3: Identify the cycle boundary based on the charge / discharge cycle status bit, divide the time series data of the associated group into several cycle segments according to the cycle boundary, calculate the aggregate value for each cycle segment according to the preset time granularity and store it; S4: Sort the completion time of the loop segments in descending order, and retain the aggregate value and difference data of the loop segments whose sorting number is less than or equal to the first preset value; for the loop segments whose sorting number is greater than the first preset value, only retain the loop summary information of the loop segment, and delete the data in the loop segment other than the loop summary information; repeat S1-S4 to process the measurement point data in real time. The cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for this cycle segment; In addition to the loop summary information, the data includes aggregated values ​​and difference data within the loop segment.

2. The data storage method for measurement points in an energy storage power station according to claim 1, characterized in that, The specific implementation of S1 includes the following steps: S1.1: Obtain the electrical topology data of the energy storage power station. The electrical topology data includes: equipment list, electrical connection relationship of equipment, and secondary control signal relationship; S1.2: Analyze the electrical topology data item by item to identify the electrical physical correlation characteristics between measurement points; S1.3: Based on the electrical and physical correlation characteristics between the identified measurement points, the measurement points are divided into association groups, and measurement points belonging to the same electrical and physical correlation characteristics are divided into the same association group; each association group contains no less than two measurement points; S1.4: Establish location binding relationships between the divided association groups and the storage partition units in the time series database.

3. The data storage method for measurement points in an energy storage power station according to claim 2, characterized in that, The electrical-physical correlations include series voltage correlations, three-phase current symmetry correlations, power conservation correlations, and control-response correlations.

4. The data storage method for measurement points in an energy storage power station according to claim 3, characterized in that, The specific implementation method of S2 includes the following steps: S2.1: Obtain the aligned time-series data block of the test points within the same associated group; S2.2: In the aligned time series data block, select the numerical sequence of one measurement point in the associated group as the reference column; S2.3: For each measurement point in the associated group other than the reference column, calculate the difference sequence between its numerical sequence and the reference column to obtain the difference data matrix; S2.4: Perform threshold judgment on each difference in the difference data matrix and update the difference data matrix according to the threshold judgment result; Based on the threshold judgment result, a marker area is set at the front of each row of data in the difference data matrix. The marker area is used to indicate whether each difference is stored as difference data or the original value at this timestamp. S2.5: Write the processed difference data matrix into the time series database in columnar storage mode.

5. The data storage method for measurement points in an energy storage power station according to claim 4, characterized in that, The specific implementation method of S3 includes: S3.1: Obtain the charge / discharge cycle status data of the energy storage power station; The charge / discharge cycle status bit data includes: charging state, discharging state, and standby state; S3.2: Scan and identify the charge and discharge cycle status data to identify the cycle boundaries of the energy storage power station and divide the charge and discharge cycle segments. Each charge / discharge cycle segment includes the following attributes: cycle segment number, cycle segment start timestamp, and cycle segment end timestamp; S3.3: Divide the time series data of the associated group according to the cycle boundary of the charge-discharge cycle segment, and classify the difference data matrix and the original numerical sequence of the benchmark column belonging to the same time range of the charge-discharge cycle segment into the data set of that charge-discharge cycle segment; S3.4: Calculate the aggregated value for the data set of each charge-discharge cycle segment according to the preset time unit. The aggregated value types include second-level aggregated value, minute-level aggregated value, and hour-level aggregated value. S3.5: Store the calculated aggregate value into the time series database and maintain the cyclic segment index information.

6. The data storage method for measurement points in an energy storage power station according to claim 5, characterized in that, The aggregated values ​​at the second, minute, and hour levels all include the mean, maximum, minimum, and cumulative values. Aggregate value calculations include: second-level calculations, minute-level calculations, and hour-level calculations; The second-level aggregated value is obtained by calculating the mean, maximum, minimum and cumulative values ​​of the original values ​​of each measuring point within a 1-second time window. The minute-level aggregated value is obtained by calculating the minute-level aggregated value. Specifically, a second-level aggregated value is calculated within a 1-minute time window. The hourly aggregate value is obtained by calculating the hourly aggregate value. Specifically, the minute-level aggregate value within the time window is calculated twice, with a time window of 1 hour.

7. The data storage method for measuring points in an energy storage power station according to claim 6, characterized in that, The specific implementation method of S4 includes: S4.1: Read the cyclic segment index information from the time series database; S4.2: For each associated group, sort all its loop segment index information in descending order according to the loop segment end timestamp to obtain a list of loop segment indexes in descending order; S4.3: Perform differential retention on data in different loop segments based on the descending order of the serial number and the first preset value; S4.4: After completing the differentiation retention, update the circular segment index information of the associated group.

8. The data storage method for measuring points in an energy storage power station according to claim 7, characterized in that, In step S4.3, the data of different loop segments are differentially retained according to the descending order of the sequence number and the first preset value. The specific implementation method includes: For cyclic segments with descending order and serial numbers less than or equal to the first preset value, all data of the cyclic segment is retained, including: cyclic summary information, aggregated values ​​of each time unit in the aggregated record, difference data matrix, and original numerical sequence of the benchmark column; among which, the cyclic summary information is extracted from the aggregated values ​​and retained as well. For loop segments with indexes greater than the first preset value in descending order, perform the following processing: Extract and save the cycle summary information for this cycle segment; the cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for this cycle segment; Delete all data in this loop segment except for the loop summary information, including: aggregated values ​​for each time unit, difference data matrix, and original numerical sequence of the baseline column; Release the storage space occupied by the deleted data.

9. A method for storing measurement point data in an energy storage power station according to claim 8, characterized in that, The methods for selecting the baseline column include: The numerical sequence of the first measurement point within the associated group is selected as the baseline column; The measurement point with the smallest variance in the numerical sequence within the associated group is selected as the baseline column; Select the measurement point that is first or last in the equipment code sorting within the associated group as the baseline column.

10. A data storage system for measuring points in an energy storage power station, used to execute the data storage method for measuring points in an energy storage power station as described in any one of claims 1-9, characterized in that, include: The association grouping module is used to obtain the electrical and physical correlation between various measurement points in the energy storage power station. Based on the electrical and physical correlation, the measurement points are divided into no less than two association groups, and the measurement point data of the same association group are stored in the time series database to the same virtual node. The cross-column compression module is used to select the numerical sequence of one of the measurement points as the reference column for time series data of multiple measurement points within the same association group, calculate the difference data between the numerical sequences of the remaining measurement points and the reference column at the same timestamp, and use the difference data to replace the original numerical sequences of the remaining measurement points for storage. The cycle aggregation module is used to identify cycle boundaries based on the charge / discharge cycle status bit, divide the time-series data of the associated group into multiple cycle segments according to the cycle boundaries, calculate and store the aggregation value for each cycle segment according to a preset time unit; The lifecycle management module is used to sort the completion time of loop segments in descending order, retain the aggregate value and difference data of loop segments whose sorting number is less than or equal to the first preset value; for loop segments whose sorting number is greater than the first preset value, only the loop summary information of the loop segment is retained, and the data in the loop segment other than the loop summary information is deleted. The cycle summary information includes the total charging energy, total discharging energy, average charging power, average discharging power, maximum voltage, and minimum voltage for this cycle segment; In addition to the loop summary information, the data includes aggregated values ​​and difference data within the loop segment.

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