Current transformer connection detection method, device, system and storage medium

Abstract

The application relates to the technical field of parallel operation energy storage systems, and discloses a current transformer connection detection method, device and system and a storage medium. The selected phase of the parallel operation energy storage system is controlled to separately output power and perform dynamic change regulation, the correlation of power change amounts is analyzed according to the inversion power change amount of each energy storage inversion unit, the power grid power change amount and a detection model constructed, and the connection state of the CT is determined phase by phase. Compared with traditional manual detection or fixed threshold judgment, the method has high automation degree, can accurately identify the forward connection, reverse connection, wrong connection and missing connection of the CT in real time, abnormality can be found in time, and system failure or performance decline caused by connection errors can be avoided. The intelligent detection technology is adopted, the need for traditional auxiliary equipment is eliminated, and the detection efficiency and system reliability are improved.

Description

Current transformer connection detection methods, devices, systems and storage media Technical Field

[0001] This application relates to the field of parallel energy storage system technology, and in particular to a current transformer connection detection method, device, system and storage medium. Background Technology

[0002] With the rapid development of energy storage technology, energy storage systems play a crucial role in distributed energy resources, microgrids, and user-side load dispatching. In these systems, electricity meters are a key component, especially current transformers (CTs), which are used not only to measure grid current but also to enable self-consumption of system power and reverse current prevention. However, in practical applications, if the meter's CT is connected incorrectly, the energy storage system will collect erroneous data, leading to incorrect power dispatching and ultimately causing abnormal system operation.

[0003] Currently, methods for testing whether CT wiring is normal usually require manual wiring adjustment or comparison through simulated load in an off-grid state, which increases additional complexity and equipment requirements, causing inconvenience to users.

[0004] Besides the possibility of incorrect CT wiring, for parallel systems, there may also be a situation where the power line of an inverter on the grid side is connected incorrectly, that is, the phase sequence of the three phases of an inverter is inconsistent with that of the grid. For example, the L1 phase of inverter B is connected to the L2 phase of the grid. This kind of incorrect wiring will cause circulating current between inverters, and in severe cases, it may even lead to the risk of phase-to-phase short circuit. Summary of the Invention

[0005] The embodiments of this application mainly address the technical problem of how to intelligently detect the connection status of the current transformer of the energy storage system meter and the phase sequence connection status of the current phase between energy storage inverter units (connection status of the power line at the grid end) without relying on auxiliary equipment.

[0006] To solve the above-mentioned technical problems, one technical solution adopted in this application is: providing a current transformer connection detection method, applied to a parallel energy storage system, wherein the parallel energy storage system includes multiple energy storage inverter units, a power grid, and current transformers. The AC output terminals of each phase of the multiple energy storage inverter units are connected in parallel to the power grid, and the sampling side of each phase of the current transformer is connected to the power grid, and the feedback side is connected to one of the multiple energy storage inverter units; the method includes: S1. controlling the selected phase output power of each energy storage inverter unit individually and dynamically adjusting it; S2. acquiring the inverter power of each energy storage inverter unit. S3. Based on the inverter power data and the grid power data, perform power difference calculation processing to obtain the inverter power change data and grid power change data of each energy storage inverter unit; S4. Construct a current transformer connection detection model, and obtain target data based on the inverter power change data of each energy storage inverter unit, the grid power change data and the detection model; S5. Perform connection identification of the current phase of the current transformer based on the target data; S6. Change the selected phase of each energy storage inverter unit and repeat steps S1-S5 to perform connection identification of each phase of the current transformer.

[0007] In some embodiments, step S2 includes: obtaining a data sampling period and a data sampling number according to a preset custom rule; obtaining inverter power data of each energy storage inverter unit according to the data sampling period and the data sampling number; and obtaining grid power data through the current transformer according to the data sampling period and the data sampling number.

[0008] In some embodiments, step S3 includes: calculating the difference between the inverter power at the current acquisition time and the inverter power at the previous acquisition time based on the inverter power data to obtain initial inverter power change data; performing filtering processing on the initial inverter power change data to obtain the inverter power change data; calculating the difference between the grid power at the current acquisition time and the grid power at the previous acquisition time based on the grid power data to obtain initial grid power change data; performing the filtering processing on the initial grid power change data to obtain the grid power change data.

[0009] In some embodiments, step S4 includes: defining a multiple linear regression model based on the inverter power change data of each energy storage inverter unit and the grid power change data, wherein the independent variable of the multiple linear regression model is the inverter power change of each energy storage inverter unit and the dependent variable is the grid power change; solving for each regression coefficient based on the multiple linear regression model, and using each regression coefficient as the target data.

[0010] In some embodiments, step S5 includes: if each regression coefficient meets a first preset detection range, then the connection state of the current phase of the current transformer is determined to be a forward connection; if each regression coefficient meets a second preset detection range, then the connection state of the current phase of the current transformer is determined to be a reverse connection; if each regression coefficient meets a third preset detection range, then the connection state of the current phase of the current transformer is determined to be an incorrect connection or a missing connection.

[0011] In some embodiments, step S5 further includes: identifying the phase sequence connection of the current phase among multiple energy storage inverter units based on the target data (identifying the connection of the current phase of the energy storage inverter unit to the grid based on the target data); step S6 further includes: changing the selected phase of each energy storage inverter unit and repeating steps S1-S5 to identify the phase sequence connection of each phase among multiple energy storage inverter units.

[0012] In some embodiments, if the difference between the regression coefficients meets the fourth preset detection range, then it is determined that the current phase sequence is incorrectly connected between the plurality of energy storage inverter units.

[0013] To solve the above-mentioned technical problems, another technical solution adopted in this application is: providing a current transformer connection detection device, the device comprising: a control module, the control module being used to control the selected phase of each energy storage inverter unit to output power individually and perform dynamic adjustment; an acquisition module, the acquisition module being used to acquire inverter power data and grid power data of each energy storage inverter unit; a calculation module, the calculation module being used to perform power difference calculation processing based on the inverter power data and the grid power data to obtain inverter power change data and grid power change data of each energy storage inverter unit; a construction module, the construction module being used to construct a current transformer connection detection model, and obtain target data based on the inverter power change data of each energy storage inverter unit, the grid power change data and the detection model; and an identification module, the identification module being used to identify the connection of the current transformer's current phase based on the target data.

[0014] To solve the above-mentioned technical problems, another technical solution adopted in the embodiments of this application is: to provide a parallel energy storage system, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described above.

[0015] To solve the above-mentioned technical problems, another technical solution adopted in the embodiments of this application is: providing a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by the parallel energy storage system, the parallel energy storage system performs the method described above.

[0016] Unlike related technologies, this application provides a method for detecting current transformer (CT) connections. By controlling and dynamically adjusting the output power of selected phases in a parallel energy storage system, and analyzing the correlation between power changes based on the inverter power changes of each energy storage inverter unit, the grid power changes, and a constructed detection model, the connection status of the CT is determined phase by phase. Compared to traditional manual detection or fixed threshold judgment, this method is highly automated and can accurately identify correct, reverse, incorrect, and missing connections of the CT in real time, promptly detecting anomalies and preventing system failures or performance degradation due to connection errors. The use of intelligent detection technology eliminates the need for traditional auxiliary equipment, improving detection efficiency and system reliability.

[0017] In a further proposed solution, the detection model employs a multiple linear regression model. The change in inverter power of each energy storage inverter unit is used as the independent variable, and the change in grid power is used as the dependent variable. By solving for the regression coefficients using the least squares method, not only can wiring problems in the current transformer (CT) itself be detected, but phase sequence mismatch between the inverter and the grid can also be effectively diagnosed. This dual-protection mechanism can fundamentally prevent safety hazards such as circulating current and phase-to-phase short circuits caused by wiring errors, elevating the system's safety level to a new level. Attached Figure Description

[0018] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0019] Figure 1 is a schematic block diagram of a parallel energy storage system provided in an embodiment of this application;

[0020] Figure 2 is a schematic diagram of the inverter power variation and grid power variation provided in the embodiments of this application;

[0021] Figure 3 is a flowchart of a current transformer connection detection method provided in an embodiment of this application;

[0022] Figure 4 is a schematic block diagram of a current transformer connection detection device provided in an embodiment of this application;

[0023] Figure 5 is a schematic diagram of the hardware structure of the parallel energy storage system for implementing the current transformer connection detection method provided in the embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] It should be noted that, unless otherwise specified, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device schematic diagram or the order in the flowchart.

[0026] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0027] Please refer to Figure 1, which is a schematic block diagram of a parallel energy storage system provided in an embodiment of this application. As shown in Figure 1, taking a three-phase parallel energy storage system as an example, the parallel energy storage system 100 includes: multiple energy storage inverter units 10, a power grid 20, and current transformers (CTs) 30. Each energy storage inverter unit 10 includes a first inverter and a second inverter (INV1 and INV2 in Figure 1). The AC output terminals (power grid terminals) of each phase of the multiple energy storage inverter units 10 are connected in parallel to the power grid 20. For example, the L1 phase of INV1 and the L1 phase of INV2 are connected in parallel to the L1 phase of the power grid, and the same applies to other phases. Each phase sampling side of the CT 30 is connected to each phase of the power grid, and the feedback side is connected to one of the multiple energy storage inverter units. For example, the L1, L2, and L3 phases of the CT are connected to the L1, L2, and L3 phases of the power grid 30, respectively, to detect the currents I1, I2, and I3 of the three phases of the power grid. The feedback side of the CT30 can be connected to the INV1 via COM (Communication Port, serial communication interface).

[0028] In this embodiment, the first inverter 11 and the second inverter 12 are core components of the parallel energy storage system 100, responsible for converting the DC power stored in the energy storage battery (not shown) into AC power suitable for the grid 20 or the load. Each inverter has three-phase outputs (L1, L2, L3), enabling it to provide three-phase AC power to the grid 20 or charge it according to the grid 20's needs. Its operating principle is as follows: the inverter controls the DC current through pulse width modulation via power electronic switches to control the output voltage and frequency. When the inverter is in grid-connected mode, it synchronizes with the grid's voltage and frequency; in off-grid mode, it independently controls the output voltage and frequency. Furthermore, the inverter monitors the output power through data acquisition devices (such as meters and current transformers 30) and adjusts the output power according to grid demand or energy storage requirements.

[0029] Grid 20 serves as the energy exchange platform for the parallel energy storage system 100, connecting the parallel energy storage system to the external power grid and allowing bidirectional flow of electrical energy. The grid provides the electrical energy required by external loads and also accepts the electrical energy generated by the parallel energy storage system during discharge. The interconnection between the parallel energy storage system and the grid is achieved through an inverter, which can adjust the charging and discharging of the system according to the power demand of the grid. Its working principle is as follows: the grid provides AC power to users through its complex transmission and distribution system. When the parallel energy storage system is connected to the grid, the inverter outputs the stored electrical energy in a form adapted to the grid, acting as an energy source for the grid. When the grid load increases or the power demand is too high, the inverter of the parallel energy storage system will supply power to the grid to help balance the grid load; conversely, when the grid has excess power, the parallel energy storage system will charge to store the excess energy.

[0030] The current transformer 30 (CT in Figure 1) measures current and converts it into a standard signal usable by monitoring and protection systems. Current transformers are commonly used in energy storage systems to monitor the direction and magnitude of current flow in the grid and inverters. Through the current transformer, the system can obtain accurate power data, aiding in power dispatch and anomaly detection. Its working principle is as follows: the current transformer provides safe current monitoring by converting a large current into a smaller one. Its basic principle is through electromagnetic induction, guiding the current to a magnetic core, thereby inducing a current proportional to the original current. This current signal is transmitted to the meter or control system for processing and monitoring via a sensor. When the CT is wired correctly, the meter can accurately measure the current; however, if the wiring is incorrect (such as reverse connection or missing connection), the system will obtain incorrect current data, thus affecting power calculation and system dispatch.

[0031] In practical applications, several situations may arise when a phase of the current transformer (CT) is incorrectly connected. Please refer to Figure 2, which is a schematic diagram of the system inverter power change and grid power change provided in this embodiment of the application, revealing the connection status of the current transformer. Figure 2(a) illustrates the system inverter power change and grid power change when a phase of the CT is missing or incorrectly connected; Figure 2(b) illustrates the system inverter power change and grid power change when a phase of the CT is reversed; and Figure 2(c) illustrates the system inverter power change and grid power change when the inverter phase sequence and grid phase sequence are inconsistent. The horizontal axis represents time T, and the vertical axis represents power P. For example, in (a), the inverter power changes, but the grid power remains unchanged, revealing a missing or incorrect connection of a phase of the CT. A missing connection refers to a missing connection of a phase of the meter CT (failing to connect to any phase in the grid). This situation usually prevents the parallel energy storage system from obtaining the current signal of that phase, thus causing the grid power change to not accurately reflect the inverter power change. Misconnection refers to a phase of the current transformer (CT) in the electricity meter being incorrectly connected to another phase of the power grid. When the inverter selects a phase to output power, but other phases have no power output, the CT in that phase will not collect any grid current. Omission or misconnection specifically manifests as the grid power of a certain phase remaining almost constant, even though the inverter's power in that phase changes, leading to incorrect system power scheduling. For example, in (b), the inverter power and grid power change in opposite directions and proportionally, revealing a misconnection of a CT. Reverse connection means the CT's wiring direction is opposite to the current flow direction. A reverse-connected CT will cause the collected current signal to be in the wrong direction, thus causing errors in power calculation. During discharge, the grid power changes in the opposite direction, which may be misinterpreted as the energy storage system charging, causing the inverter to discharge or charge incorrectly, thus affecting battery management and grid interaction. For another example, in (c), the inverter power and grid power change in the same direction, but the magnitude of the change is inconsistent, revealing an inverter anomaly: there is a phase sequence mismatch between the inverter and the grid in the grid-connected inverter. This situation can lead to inconsistencies between grid power and inverter power changes. Even if the inverter power changes, the magnitude of the grid power change may be small, or the proportional relationship may be unbalanced. When the grid phase sequence of different inverters in a parallel system is inconsistent, although the phase difference between phases of a single inverter is normal, the different phase sequences of the inverters (e.g., the first inverter is connected to L1\L2\L3, and the second inverter is connected to L2\L3\L1) will cause a deviation between the changing trends of grid power and inverter power. In this case, the changes in grid power and inverter power are no longer completely synchronized, leading to chaotic grid load dispatch and affecting system stability.

[0032] Understandably, in energy storage systems, inverter power and grid power changes are strongly correlated. Inverter power is actively controlled by the system, and its changes typically directly affect grid power. Assuming constant load power, grid power changes are primarily influenced by inverter power. Under normal circumstances, inverters dynamically adjust their output power according to demand, so grid power changes are generally synchronized with inverter power changes. Especially in parallel systems, the power changes of multiple inverters are combined and reflected in the grid power. Since several types of CT (current transformer) connection errors (such as missing, reverse, or incorrect connections) and inconsistencies between inverter and grid phase sequences can affect the relationship between inverter power and grid power, a tool is needed to effectively analyze the relationship between inverter power and grid power changes in parallel systems. Understandably, multiple linear regression analysis can construct a linear relationship between inverter power and grid power through a model. The goal of the regression model is to determine the degree of influence of inverter power on grid power changes (i.e., the regression coefficient). Under normal circumstances, grid power should change by a fixed proportion with inverter power changes, i.e., the regression coefficient should be close to 1. Therefore, multiple linear regression can be used to analyze and detect CT wiring errors (such as missing, incorrect, or reverse connections) and phase sequence mismatch between the inverter and the grid, as these errors cause the regression coefficients to deviate significantly from 1, thus reflecting inconsistencies in power measurement. Regression analysis can automatically detect these deviations and provide quantitative anomaly diagnosis. By continuously collecting data and performing regression analysis, the system can promptly detect incorrect CT connections or phase sequence mismatch between the inverter and the grid, thereby achieving automated fault detection and correction.

[0033] Therefore, this application provides a current transformer connection detection method, applied to a parallel energy storage system 100. Please refer to Figure 3, which is a flowchart of a current transformer connection detection method provided in this application. As shown in Figure 3, the method includes:

[0034] S1: Controls the output power of selected phases of each energy storage inverter unit individually and dynamically adjusts them.

[0035] For parallel energy storage systems, the output power of a certain phase (such as phase L1) of each energy storage inverter unit is independently controlled, while other phases do not output power (such as phases L2 and L3). The power value of these phases changes dynamically according to a preset rule (step, ramp, or sinusoidal fluctuation).

[0036] The output power of the energy storage inverter unit can be positive or negative. The positive direction is defined as the power transmitted from the energy storage inverter unit to the grid, and the negative direction is the power fed back from the grid to the energy storage inverter unit.

[0037] S2: Obtain inverter power data and grid power data for each energy storage inverter unit.

[0038] Acquiring inverter power data and grid power data includes: obtaining the data sampling period and number of data samplings according to preset custom rules; acquiring inverter power data of each energy storage inverter unit according to the data sampling period and number of data samplings; and acquiring grid power data of the power grid through a current transformer according to the data sampling period and number of data samplings.

[0039] First, pre-set custom rules based on system requirements and the working environment. These custom rules include the sampling period and the number of samplings. Understandably, acquiring power data requires determining the sampling period (e.g., per second, per minute), which determines the time interval between each data collection. For accurate analysis of inverter power and grid power, the sampling period should be as short as possible to capture minute changes in system operation. Next, the number of samplings needs to be determined. Set the number of data collections required for each detection based on the actual situation (e.g., 30 or more times). The number of samplings determines the statistical precision of the detection results; a higher number of samplings helps reduce random errors and improve detection accuracy.

[0040] Secondly, according to the set sampling period and number of samplings, each energy storage inverter unit monitors its own power data in real time and feeds it back to the main energy storage inverter unit. The inverter power data is usually in units of three-phase power (L1, L2, L3) and may include forward discharge (output) or reverse charging (input) conditions. During the sampling process, the inverter power data is recorded and saved after each sampling.

[0041] Secondly, the current of each phase of the grid is collected in real time by the CT to calculate the power of each phase of the grid. The grid power data reflects the power inflow and outflow of the grid, and usually includes the three-phase power of the grid. The CT monitors the grid current and converts it into appropriate signals for the system to analyze. The magnitude and direction (positive or negative) of the grid power changes directly affect the power dispatch of the energy storage system.

[0042] Finally, during each sampling, the collected inverter power and grid power data are stored in a local database or cloud storage system. After each sampling period, the system records the inverter power and grid power at each moment and continuously collects data according to preset rules and frequency until the data analysis requirements are met.

[0043] This embodiment employs a precise sampling strategy to ensure that the energy storage system can acquire inverter power and grid power data in a timely and continuous manner during the testing process. By setting appropriate sampling periods and sampling frequencies, the acquired data is ensured to be sufficiently representative and accurate, providing a reliable data foundation for subsequent current transformer connection testing. This process not only improves testing accuracy but also effectively reduces errors caused by incomplete data or excessively low sampling frequencies, thereby ensuring the accuracy and reliability of subsequent analysis. Furthermore, the automation and real-time nature of data acquisition enhances the overall efficiency of the system and reduces human intervention.

[0044] S3: Based on the inverter power data and grid power data, perform power difference calculation to obtain the inverter power change data and grid power change data for each energy storage inverter unit.

[0045] Based on the inverter power data of each energy storage inverter unit and the grid power data, power difference calculation is performed to obtain the inverter power change data of each energy storage inverter unit and the grid power change data. This includes: calculating the difference between the inverter power at the current acquisition time and the inverter power at the previous acquisition time based on the inverter power data of each energy storage inverter unit, to obtain the initial inverter power change data; filtering the initial inverter power change data to obtain the final inverter power change data; and calculating the difference between the grid power at the current acquisition time and the grid power at the previous acquisition time based on the grid power data, to obtain the initial grid power change data; and filtering the initial grid power change data to obtain the final grid power change data.

[0046] First, each energy storage inverter unit calculates the difference between the inverter power at the current acquisition time and the inverter power at the previous acquisition time based on the collected inverter power data (the inverter power data obtained in step S102), that is:

[0047] ;

[0048] in, This represents the change in inverter power of the nth energy storage inverter unit at the i-th data acquisition time. This represents the inverter power of the nth energy storage inverter unit at the i-th data acquisition time. This represents the inverter power collected in the last time by the nth energy storage inverter unit.

[0049] Secondly, the obtained multiple inverter power variation data (i.e., the initial inverter power variation data) are screened. Screening criteria may include removing minor variations (e.g., fluctuations less than 50W) or noisy data to ensure the accuracy of subsequent analysis. After screening, the obtained inverter power variation data will be used for further analysis.

[0050] Secondly, similar to the calculation of inverter power change, difference calculation is performed on the grid power data. The difference between the grid power at the current acquisition time and the grid power at the previous acquisition time is calculated sequentially:

[0051] ;

[0052] in, This represents the change in grid power at the i-th data collection time. This indicates the power grid power at the current data collection time. This represents the grid power at the previous moment.

[0053] In addition, the obtained multiple grid power change data (initial grid power change data) are filtered, similar to the inverter power data filtering, to remove some meaningless minor changes (such as fluctuations less than 50W). The filtered grid power change data will more clearly reflect the actual trend of grid power change.

[0054] Finally, the filtered inverter power change data and grid power change data are stored. For subsequent analysis, the data needs to be categorized and labeled to ensure that each collected power data point corresponds to a specific timestamp, making it available for analysis using models such as multiple linear regression. It should be noted that when performing the power difference calculation process described above, power data and power change data can be obtained and calculated independently based on the number of phases in the energy storage system; that is, calculation can be performed once sufficient data is collected for any phase.

[0055] This embodiment accurately obtains power change data at each acquisition moment by calculating and filtering the difference between inverter power data and grid power data. By eliminating minor fluctuations or noise data, errors can be effectively reduced, ensuring data stability and reliability. Furthermore, this process provides high-quality input data for subsequent multiple linear regression analysis, ensuring the accuracy and effectiveness of the analysis results. Through precise processing of power change data, the system can more efficiently detect the connection status of current transformers, quickly identify any possible connection errors or anomalies, and improve the stability and operating efficiency of the energy storage system.

[0056] S4: Construct a current transformer connection detection model, and obtain target data based on the inverter power change data of each energy storage inverter unit, the grid power change data, and the detection model.

[0057] Specifically, this includes: defining a multiple linear regression model based on the inverter power change data of each energy storage inverter unit and the grid power change data, wherein the independent variable of the multiple linear regression model is the inverter power change of each energy storage inverter unit, and the dependent variable is the grid power change; solving for each regression coefficient based on the multiple linear regression model, and using each regression coefficient as the target data.

[0058] First, a multiple linear regression model needs to be defined based on the inverter power change data of each energy storage inverter unit and the grid power change data. Taking two energy storage inverter units as an example, a binary linear regression model is defined, the basic form of which is:

[0059]

[0060] in, Let be the change in grid power at the i-th data collection time. Let be the change in inverter power at the i-th sampling time of the first inverter. This represents the change in inverter power at the i-th sampling time of the second inverter. It is the constant term (bias term) of the regression model. and It is a regression coefficient, representing the degree of influence of inverter power change on grid power change.

[0061] Secondly, the multiple linear regression model is solved to obtain the regression coefficients. For example, the least squares method is used to solve for the regression coefficients. The least squares method determines the regression coefficients by minimizing the sum of squared errors between the predicted and actual values. The steps include: 1) calculating the error of the regression model for each set of collected data; 2) minimizing the sum of squared errors to obtain the best-fit regression coefficients. and 3) Calculate the bias term and and They are used together in the construction of regression models.

[0062] Finally, the regression coefficients and This coefficient serves as the target data, indicating the proportional relationship between the inverter power variation of each energy storage inverter unit and the grid power variation. Theoretically, under normal circumstances... and It should be close to 1 because the change in inverter power and grid power should be proportional. If and A deviation from 1 may indicate an anomaly in the CT connection, such as incorrect, missing, or reversed connection; or a phase sequence mismatch between the inverter and the grid (i.e., different phase sequences between inverters, such as the first inverter connected to L1\L2\L3, and the second inverter connected to L2\L3\L1). It should be noted that this embodiment does not limit the solution algorithm; it can be the least squares method, ridge regression, Lasso regression, etc.

[0063] This embodiment effectively captures the linear relationship between inverter power and grid power by constructing a regression model based on changes in inverter power and grid power. Through regression analysis, it is possible to accurately determine whether the current transformer connection is normal. Regression coefficients. and This target data provides a quantitative standard. When this coefficient deviates from the normal range, the system can automatically identify potential problems such as incorrect or missing connections in the current transformer, or proportional anomalies, as well as phase sequence mismatch between the inverter and the grid. This not only improves the automation and accuracy of fault diagnosis but also reduces manual intervention, optimizing the system's operating efficiency and stability.

[0064] S5: Identify the connection of the current phase of the current transformer based on the target data.

[0065] It is understandable that after obtaining the target data ( and After that, the connection status of the current transformer is accurately determined based on this value. Ideally, when... and When both are 1, the change in inverter power is directly proportional to the change in grid power. In reality, if the current transformer (CT) is connected correctly, these two coefficients should also be around 1. If there is a significant difference, it indicates that the CT is incorrectly connected or missing. For example, when... and satisfy and (When, it indicates that the current transformer is connected in the forward direction (normal); when and satisfy and When , it indicates that the current transformer is connected in reverse; when and satisfy If the connection is incorrect, it indicates that the current transformer is either incorrectly connected or missing.

[0066] Besides detecting wiring problems within the CT itself, the absolute values ​​of the differences between the regression coefficients can also effectively diagnose phase sequence mismatch between the inverter and the power grid. and satisfy If the threshold value is displayed at this time, it indicates that the phase sequence connection of the grid power line of the energy storage system is incorrect. Specifically, this means that the phase sequence of the three phases of a certain inverter is inconsistent with that of the grid, or that the current phase sequence of multiple energy storage inverter units of the inverter is incorrectly connected. It should be noted that the threshold range can be adaptively set according to the actual situation and is not limited here.

[0067] This embodiment, through precise verification and analysis of target data, combined with multiple preset threshold ranges, can effectively identify the connection status of current transformers and phase sequence mismatch issues between inverters and the power grid, thereby avoiding equipment failures or operational instability caused by connection errors. By setting different status ranges such as forward connection, reverse connection, incorrect connection, missing connection, and phase sequence error connection, the system can detect and determine the operating status of current transformers or inverters in real time, ensuring their normal operation. Furthermore, automatic identification based on detection results can reduce the risks of manual operation, improve equipment operating efficiency and safety, and is of great significance for the maintenance and management of power systems.

[0068] In some embodiments, the method further includes: providing risk warnings for the various identification results mentioned above, such as providing warnings using different display modes / indicator lights; and controlling the alarm unit to issue an alarm when the grid terminal of the CT or inverter is not correctly connected.

[0069] Understandably, based on the identification of the current transformer connection status, further visualization and alarm functions for the CT or inverter are achieved by judging the type of test result and controlling the indicator lights and alarm system. The specific process is as follows:

[0070] First, through the aforementioned data verification steps, the connection status of the current transformer (forward connection, reverse connection, incorrect connection) or the inverter's grid-side phase sequence error is determined, and the corresponding connection type is obtained. Second, based on the connection type, the control indicator light (not shown in Figure 1) displays different modes to help operators intuitively understand the current equipment's operating status. If the CT is forward connected, the control indicator light illuminates in the first display mode (e.g., solid green), indicating a normal connection; if the CT is reverse connected, the control indicator light illuminates in the second display mode (e.g., flashing red), reminding the user of a reverse connection; if the CT is incorrectly connected or missing, the control indicator light illuminates in the third display mode (e.g., solid yellow warning light), warning of a connection error; if the inverter's phase sequence error is detected, the control indicator light illuminates in the fourth display mode (e.g., flashing blue), indicating an incorrect grid-side phase sequence connection of the inverter. Additionally, when determining the type, if the CT is reversely connected, incorrectly connected, or missing, or if the inverter's grid-side phase sequence is incorrect, an alarm unit (not shown in Figure 1) is triggered to issue an alarm. The alarm unit can alert personnel through sound or other warning signals, ensuring timely detection and correction of connection problems. Furthermore, the alarm system can be set with different alarm levels based on severity. For example, a reverse connection may trigger a milder alarm, while an incorrect connection or phase sequence error may trigger a stronger alarm. Finally, through indicator lights and alarm feedback, maintenance personnel can quickly determine if there are wiring problems with the current transformer and take appropriate measures, such as adjusting the wiring or conducting inspections, to ensure stable equipment operation.

[0071] This embodiment effectively presents different connection statuses to operators in a visual manner by real-time monitoring and judgment of the current transformer connection status. Through different indicator light display modes, staff can intuitively understand the equipment's operating status, promptly identify problems, and make adjustments, reducing the incidence of human error. Furthermore, the addition of an alarm unit further enhances system safety, enabling rapid warnings in the event of non-positive connections, ensuring the stability of the current transformer and the entire system. This method not only improves equipment management efficiency but also enhances operators' responsiveness, reducing the occurrence of potential faults and accidents.

[0072] In some embodiments, the method further includes: uploading the test results to the server, so that the server receives the test results, performs data parsing, obtains the parsing results, and generates a current transformer connection test report based on the parsing results.

[0073] First, in the aforementioned steps, the system has verified the connection status of the current transformer and obtained the corresponding results. The verification results are then organized according to a predetermined data format to ensure data integrity and consistency. Finally, the formatted data is uploaded to the server via a network interface (such as HTTP, FTP, etc.). The transmission method can utilize protocols such as HTTP and FTP to ensure data integrity and security.

[0074] Secondly, after receiving the uploaded verification result data, the server first performs data integrity and validity checks to ensure that the data has not been tampered with or lost. Then, it uses predefined data parsing rules to convert the raw data into structured information, such as JSON or XML format, for easier subsequent processing.

[0075] Next, based on the analysis results, the connection states of the current transformers are classified and statistically analyzed, including the quantity and proportion of each connection state. Based on the analysis results, a current transformer connection inspection report is automatically generated. The report includes: basic information: inspection time, inspectors, equipment number, etc.; inspection results: statistical data and analysis of various connection states; and recommended measures: corresponding rectification suggestions for the identified problems.

[0076] Finally, the generated test report is stored in the server's database or file system to ensure long-term data preservation and backup. The report can also be sent to relevant personnel, such as equipment maintenance personnel and management, via email, SMS, or other means as needed. Upon receiving the report, relevant personnel should rectify the issues reported and report the rectification results back to the server. After receiving the rectification feedback, the server updates the records in the database to maintain the data's up-to-dateness and accuracy.

[0077] This embodiment uploads the inspection results of the current transformer connection status to a server for data parsing and generates a detailed inspection report, enabling comprehensive monitoring and management of the equipment status. Automated data processing and report generation improve work efficiency and reduce errors and omissions from manual operations. Simultaneously, the report storage and distribution mechanism ensures timely information transmission and sharing, facilitating relevant personnel to promptly understand the equipment status, take necessary maintenance measures, and ensure the safe and stable operation of the power system.

[0078] This application provides a method for detecting the connection status of current transformers in energy storage systems. By calculating the power difference between inverter power data and grid power data and constructing a regression model, the connection status of current transformers in energy storage systems can be accurately identified. This method can monitor the connection status of current transformers in real time, promptly detect anomalies, and avoid system failures or performance degradation due to connection errors. Employing intelligent detection technology eliminates the need for traditional auxiliary equipment, improving detection efficiency and system reliability. Furthermore, by judging different detection ranges and types, it can accurately distinguish between forward connection, reverse connection, incorrect connection, or missing connection of the current transformer (CT), as well as incorrect phase sequence connection at the grid end of the inverter. This provides important basis for system maintenance and fault diagnosis, demonstrating significant practical value and economic benefits. It should be noted that the current transformer connection detection method provided in this embodiment is applicable to three-phase, dual-phase, and single-phase energy storage systems, and is not limited to the three-phase energy storage system exemplified above. In practical applications, a regression model can be adaptively constructed according to the type of energy storage system.

[0079] Based on the current transformer connection detection method provided in the above embodiments, this application further provides a current transformer connection detection device. Please refer to Figure 4, which is a schematic block diagram of the current transformer connection detection device. As shown in Figure 4, the device 200 includes: a control module 250, an acquisition module 210, a calculation module 220, a construction module 230, and an identification module 240.

[0080] The system includes: a control module 250 for controlling the selected phase output power of each energy storage inverter unit and dynamically adjusting its changes; an acquisition module 210 for acquiring inverter power data and grid power data for each energy storage inverter unit; a calculation module 220 for performing power difference calculation based on the inverter power data and grid power data to obtain inverter power change data and grid power change data for each energy storage inverter unit; a construction module 230 for constructing a current transformer connection detection model and obtaining target data based on the inverter power change data of each energy storage inverter unit, the grid power change data, and the detection model; and an identification module 240 for identifying the connection of the current transformer's current phase based on the target data.

[0081] It should be noted that the aforementioned current transformer connection detection device can execute the current transformer connection detection method provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in the embodiments of the current transformer connection detection device can be found in the current transformer connection detection method provided in the embodiments of this application.

[0082] This application also provides a parallel energy storage system. Referring to Figure 5, it shows a schematic hardware structure of a parallel energy storage system capable of executing the methods described in the above embodiments. The parallel energy storage system 100 includes: at least one processor 310; and a memory 320 communicatively connected to the at least one processor 310. Figure 5 shows an example of one processor 310. The memory 320 stores instructions executable by the at least one processor 310. These instructions, when executed by the at least one processor 310, enable the at least one processor 310 to execute the current transformer connection detection method described in the above embodiments. The processor 310 and the memory 320 can be connected via a bus or other means; Figure 5 shows an example of a bus connection.

[0083] The memory 320, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the current transformer connection detection method in the embodiments of this application. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the current transformer connection detection method described in the above embodiments.

[0084] The memory 320 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computing device. Furthermore, the memory 320 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 320 may optionally include memory remotely located relative to the processor 310, and these remote memories may be connected to the computing device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0085] The one or more modules are stored in the memory 320, and when executed by the one or more processors 310, the current transformer connection detection method described in the above embodiment is executed.

[0086] It should be noted that the current transformer connection detection method provided in the above embodiments is applied to the main controller of a parallel energy storage system. The main controller can be integrated into one of multiple energy storage inverter units as the master unit, or it can exist independently of the multiple energy storage inverter units. It can be understood that when the main controller is integrated into a single energy storage inverter unit, that unit is called the master unit, and the other energy storage inverter units act as slave units, executing the above-mentioned current transformer connection detection method through the master controller within the master unit. When the main controller exists independently of multiple energy storage inverter units, the main controller can be independent hardware (such as a PLC, industrial computer, or dedicated controller), interacting with all energy storage inverter units through a communication network (such as CAN, Modbus, or Ethernet) to execute the above-mentioned current transformer connection detection method.

[0087] The above-described product can perform the method provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the method. Technical details not described in detail in this embodiment can be found in the current transformer connection detection method described in the embodiments of this application.

[0088] This application also provides a non-volatile computer-readable storage medium storing computer-executable instructions. These instructions are executed by one or more processors to enable the at least one processor to perform the current transformer connection detection method described in the above embodiments. For example, the non-volatile computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0089] It should be noted that the embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software and a general-purpose hardware platform, or of course, using hardware. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of this application as described above, which are not provided in detail for the sake of brevity; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A current transformer connection detection method, applied to a parallel energy storage system, wherein the parallel energy storage system includes multiple energy storage inverter units, a power grid, and current transformers, wherein the AC output terminals of each phase of the multiple energy storage inverter units are connected in parallel to the power grid, the sampling side of each phase of the current transformer is connected to the power grid, and the feedback side is connected to one of the multiple energy storage inverter units, characterized in that, The method includes the following steps: S1. Controlling the selected phase of each energy storage inverter unit to output power individually and dynamically adjusting the output power; S2. Acquiring inverter power data and grid power data for each energy storage inverter unit; S3. Calculating the power difference based on the inverter power data and grid power data to obtain inverter power change data and grid power change data for each energy storage inverter unit; S4. Constructing a current transformer connection detection model, and obtaining target data based on the inverter power change data of each energy storage inverter unit, grid power change data, and the detection model; S5. Identifying the connection of the current transformer's current phase based on the target data; S6. Replacing the selected phase of each energy storage inverter unit and repeating steps S1-S5 to identify the connection of each phase of the current transformer; the connection of each phase of the current transformer includes: the current transformer... The connection status of the current phase of the current transformer is either forward connection, reverse connection, incorrect connection, or missing connection; wherein, step S4 includes: defining a multiple linear regression model based on the inverter power change data of each energy storage inverter unit and the grid power change data, wherein the independent variable of the multiple linear regression model is the inverter power change of each energy storage inverter unit, and the dependent variable is the grid power change; solving for each regression coefficient based on the multiple linear regression model, and using each regression coefficient as the target data; wherein, step S6 includes: the target data is used to identify the grid power line phase sequence connection error of the parallel energy storage system; if the absolute value of the difference between each regression coefficient meets the fourth preset detection range, then it is determined that the current phase sequence of the multiple energy storage inverter units is incorrectly connected; the fourth preset detection range is [0.4, 1].

2. The current transformer connection detection method according to claim 1, characterized in that, Step S2 includes: obtaining the data sampling period and the number of data samplings according to a preset custom rule; obtaining the inverter power data of each energy storage inverter unit according to the data sampling period and the number of data samplings; and obtaining the grid power data through the current transformer according to the data sampling period and the number of data samplings.

3. The current transformer connection detection method according to claim 1, characterized in that, Step S3 includes: calculating the difference between the inverter power at the current acquisition time and the inverter power at the previous acquisition time based on the inverter power data to obtain initial inverter power change data; filtering the initial inverter power change data to obtain the inverter power change data; calculating the difference between the grid power at the current acquisition time and the grid power at the previous acquisition time based on the grid power data to obtain initial grid power change data; and filtering the initial grid power change data to obtain the grid power change data.

4. The current transformer connection detection method according to claim 1, characterized in that, Step S5 includes: if each regression coefficient meets the first preset detection range, then the connection state of the current phase of the current transformer is determined to be a forward connection; if each regression coefficient meets the second preset detection range, then the connection state of the current phase of the current transformer is determined to be a reverse connection; if each regression coefficient meets the third preset detection range, then the connection state of the current phase of the current transformer is determined to be an incorrect connection or a missing connection.

5. The current transformer connection detection method according to claim 1, characterized in that, Step S5 further includes: identifying the phase sequence connection of the current phase among multiple energy storage inverter units based on the target data; Step S6 further includes: changing the selected phase of each energy storage inverter unit and repeating steps S1-S5 to identify the phase sequence connection of each phase among multiple energy storage inverter units.

6. A current transformer connection detection device, applied to a parallel energy storage system, the parallel energy storage system comprising multiple energy storage inverter units, a power grid, and current transformers, wherein the AC output terminals of each phase of the multiple energy storage inverter units are connected in parallel to the power grid, the sampling side of each phase of the current transformer is connected to the power grid, and the feedback side is connected to one of the multiple energy storage inverter units, characterized in that, The device includes: a control module for controlling the selected phase output power of each energy storage inverter unit individually and dynamically adjusting its changes; an acquisition module for acquiring inverter power data and grid power data for each energy storage inverter unit; a calculation module for performing power difference calculation based on the inverter power data and grid power data to obtain inverter power change data and grid power change data for each energy storage inverter unit; a construction module for constructing a current transformer connection detection model, and obtaining target data based on the inverter power change data of each energy storage inverter unit, the grid power change data, and the detection model; and an identification module for identifying the connection of the current transformer's current phase based on the target data. The connection of each phase of the current transformer includes: the current transformer... The connection status of the current phase of the current transformer is either forward connection, reverse connection, incorrect connection, or missing connection; wherein, the construction module is specifically used to: define a multiple linear regression model based on the inverter power change data of each energy storage inverter unit and the grid power change data, wherein the independent variable of the multiple linear regression model is the inverter power change of each energy storage inverter unit, and the dependent variable is the grid power change; solve for each regression coefficient based on the multiple linear regression model, and use each regression coefficient as the target data; wherein, the identification module is used to: use the target data to identify grid power line phase sequence connection errors in the parallel energy storage system; if the absolute value of the difference between each regression coefficient meets the fourth preset detection range, then determine that the current phase sequence of the multiple energy storage inverter units is incorrectly connected; the fourth preset detection range is [0.4, 1].

7. A parallel energy storage system, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-5.

8. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores computer-executable instructions that, when executed by the parallel energy storage system, cause the parallel energy storage system to perform the method described in any one of claims 1-5.

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