A voltage on-line monitoring method and system for an electric energy storage tank

Abstract

This invention relates to the field of energy storage boxes, and more specifically, to a method and system for online voltage monitoring of energy storage boxes. The method includes: acquiring voltage and current data of the energy storage box according to a preset sampling interval, and fitting them into voltage and current curves respectively; constructing a data sequence for a target point, calculating the primary stability of the data sequence, and calculating the true stability of the data sequence; generating several sub-sequences using a sliding window based on a calculated sliding step size, calculating the fuzzy entropy value, obtaining the anomaly degree of the target point, and completing online monitoring. The technical solution of this invention can improve the efficiency and accuracy of voltage monitoring results, providing a guarantee for the safe operation of energy storage boxes.

Description

Technical Field

[0001] This invention relates to the field of electrical energy storage boxes. More specifically, this invention relates to a method and system for online voltage monitoring of electrical energy storage boxes. Background Technology

[0002] As a core component of energy storage systems, the performance of the energy storage tank directly impacts the efficiency, safety, and lifespan of the entire system. Real-time voltage monitoring is crucial for ensuring the efficient operation of the energy storage tank. Voltage monitoring not only helps assess the battery's health and promptly detect abnormal voltage conditions, but also provides real-time operational data to the battery management system, enabling precise battery control. By monitoring voltage changes, the charging and discharging process can be effectively regulated, preventing overcharging or over-discharging and avoiding safety hazards caused by voltage instability. Furthermore, voltage monitoring data can be used to predict battery life, allowing for timely maintenance and replacement, thereby extending battery lifespan and improving the overall stability and reliability of the system. Therefore, real-time voltage monitoring plays a vital role in ensuring the efficient and safe operation of energy storage systems.

[0003] The existing Chinese patent application with publication number CN119199379A discloses a method and system for selecting single-phase grounding faults based on zero-sequence current mode decomposition. In this method, the IMF components of each line are granulated into linear Gaussian fuzzy information to obtain the membership function of the linear Gaussian fuzzy information granules. The fuzzy entropy is calculated based on the membership function, and the single-phase grounding fault line is determined based on the fuzzy entropy.

[0004] However, the fuzzy entropy method detects abnormal data by comparing the difference in fuzzy entropy between the current and previous voltage data sequences. Furthermore, the fixed sliding window step size leads to a large number of subsequence calculations in long data sequences, which increases the computational burden, affects the performance of the real-time monitoring system, and results in inaccurate voltage monitoring results. Summary of the Invention

[0005] To address the problem of inaccurate voltage monitoring results, this invention proposes an online voltage monitoring method and system for energy storage boxes.

[0006] In a first aspect, the present invention discloses an online voltage monitoring method for an energy storage box, comprising: acquiring voltage and current data of the energy storage box according to a preset sampling interval, and fitting them into voltage curves and current curves respectively; taking any sampling moment as a target point, constructing a data sequence of the target point, calculating the primary stability of the data sequence, and calculating the true stability of the data sequence based on the primary stability, the correlation between the current curve and the voltage curve within the data sequence, and the average sampling interval; calculating the sliding step size for each sampling moment based on the true stability, generating several sub-sequences by sliding a window based on the sliding step size, calculating the fuzzy entropy value, obtaining the adjacent fuzzy entropy value of the previous sampling moment adjacent to the target point, calculating the difference between the fuzzy entropy value of the target point and the adjacent fuzzy entropy value, and using the normalized difference as the anomaly degree of the target point to complete the online monitoring; wherein, calculating the sliding step size includes: calculating the product of the true stability of the target point and the upper limit of the sliding step size, and using the sum of the product after rounding down and the lower limit of the sliding step size as the sliding step size of the target point.

[0007] By calculating the primary stability based on the data sequence at the target point and comprehensively considering the correlation between current and voltage curves, the stability of the data sequence is evaluated, thereby achieving comprehensive control over the dynamic behavior of the system. By calculating the sliding step size at each sampling moment, the data sequence is finely divided into multiple subsequences, and the fuzzy entropy value is calculated, which accurately reflects the complexity and uncertainty of the system. Furthermore, by calculating the fuzzy entropy difference between the target point and its adjacent points, abnormal conditions of the energy storage tank can be detected in real time, identifying potential faults or abnormal fluctuations.

[0008] Preferably, the data sequence includes: constructing a data sequence based on a preset number of historical sampling times, with the target point being the endpoint of the data sequence.

[0009] Preferably, the primary stability includes: calculating the absolute difference of voltage between any two adjacent sampling times in the data sequence, and using the sum of all absolute differences as the result of negative correlation mapping as the primary stability.

[0010] The accumulation of absolute differences reflects the severity of voltage changes; the larger the difference, the more significant the voltage fluctuation. Through exponential function mapping, these fluctuations can be transformed into a stability value, where smaller voltage fluctuations correspond to higher stability, thus effectively reflecting the stability level of the energy storage tank.

[0011] Preferably, the primary stability further includes: calculating the absolute difference of voltage at any two adjacent sampling times in the data sequence, calculating the cumulative value of all absolute differences, and using the product of the cumulative value and the voltage range in the data sequence through negative correlation mapping as the primary stability.

[0012] It can not only effectively capture the trend and degree of voltage fluctuations, but also help to better evaluate the dynamic stability of power systems and optimize equipment operation and management in practical applications by comprehensively considering the rate and magnitude of voltage changes.

[0013] Preferably, the true stability includes: obtaining all voltage extreme points in the data sequence, calculating the average sampling interval of the voltage extreme points, and similarly obtaining the average sampling interval of the current extreme points in the data sequence; the true stability satisfies the following relationship: , Indicates the true stability of the data sequence. Indicates the primary stability of the data sequence. This indicates the correlation between current and voltage curves in a data sequence. This represents the average sampling interval of voltage extreme points in the data sequence. This represents the average sampling interval of current extreme points in the data sequence. This represents the normalization function.

[0014] By calculating a comprehensive index of primary stability, correlation, and extreme point interval, the true stability of a data sequence can be assessed more accurately, thereby providing more reliable stability analysis, reducing the impact of noise, and improving the actual prediction and control capabilities for the operational stability of energy storage tanks or other systems.

[0015] Preferably, the correlation is the Pearson correlation coefficient.

[0016] Preferably, the correlation is dynamic time warping.

[0017] Using dynamic time warping can more effectively handle potential time delays or asynchrony issues between the two on the timeline.

[0018] Secondly, the present invention discloses an online voltage monitoring system for an energy storage box, comprising: a processor; and a memory, the memory storing computer instructions, which, when executed by the processor, cause the system to perform the aforementioned online voltage monitoring method for an energy storage box.

[0019] The beneficial effects of this invention are:

[0020] 1. By analyzing the variation characteristics of voltage and current data, this invention can obtain the true stability of the sequence corresponding to each voltage data point, thereby providing a more accurate basis for subsequent anomaly detection.

[0021] 2. This invention dynamically adjusts the sliding window step size based on the stability index of the data, allowing for flexible adjustment of computational density within different stability regions. Specifically, regions with higher stability can employ a larger sliding step size, thereby reducing the number of calculations of fuzzy membership degrees between subsequent subsequences and lowering the computational load; while in regions with greater fluctuations, the sliding step size is reduced to ensure detection accuracy and avoid false detections.

[0022] 3. Analyzing the changing characteristics of voltage and current data can effectively reduce noise interference, especially under external interference such as electromagnetic waves. When the noise level is close to the normal data value, the optimization scheme can more accurately adjust the sliding step size, avoiding calculation deviations caused by noise. This not only improves computational efficiency but also maintains the accuracy of anomaly detection, ensuring the system's response speed and accuracy during real-time monitoring. Attached Figure Description

[0023] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0024] Figure 1 This is a flowchart of an online voltage monitoring method for an electric energy storage box according to an embodiment of the present invention. Detailed Implementation

[0025] 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, not all, of the embodiments of the present invention. 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.

[0026] It should be understood that when the terms "first," "second," etc., are used in the claims, specification, and drawings of this invention, they are only used to distinguish different objects and not to describe a specific order. The terms "comprising" and "including" used in the specification and claims of this invention indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0027] This invention provides a method for online voltage monitoring of an electrical energy storage box. For example... Figure 1 As shown, a method for online voltage monitoring of an electric energy storage box includes steps S1-S3, which are described in detail below.

[0028] S1. Obtain voltage and current data of the energy storage box according to the preset sampling interval, and fit them into voltage curves and current curves respectively.

[0029] In one embodiment, in order to accurately monitor the voltage of the energy storage box, voltage and current data are first collected by voltage and current sensors respectively, and the collection of the two is ensured to be synchronized, that is, started and stopped at the same time.

[0030] For example, the sampling frequency is set to 10 times per second and the sampling period is 1 hour. The specific sampling interval and period can be adjusted by those skilled in the art according to actual needs to ensure the accuracy and reliability of data acquisition.

[0031] The acquired analog signals are converted into digital signals using an analog-to-digital converter, resulting in digital representations of voltage and current data. Subsequently, existing curve fitting techniques are used to fit these digitized voltage and current data, generating smooth voltage and current curves. These curves more clearly reflect the operating status of the energy storage tank, providing a basis for subsequent data analysis and fault diagnosis.

[0032] S2. Take any sampling time as the target point, construct the data sequence of the target point, calculate the primary stability of the data sequence, and calculate the true stability of the data sequence based on the primary stability, the correlation between the current curve and the voltage curve in the data sequence, and the average sampling interval.

[0033] In one embodiment, the process of constructing a data sequence involves selecting a preset number of historical sampling moments to form a data sequence containing a target point, where the target point is the endpoint of the sequence. For example, the preset number is 200, meaning that 200 historical sampling moments preceding the target point are selected to construct the data sequence.

[0034] If there are fewer than 200 historical sampling points before the target point, supplement the data by collecting all data within the previous minute to ensure the integrity of the data sequence. It is important to note that this supplementary data is only used to construct the data sequence and does not participate in subsequent anomaly monitoring and analysis. This ensures that the data source of the monitoring system remains consistent and does not interfere with anomaly detection.

[0035] The calculation of primary stability includes: calculating the absolute difference of voltage between any two adjacent sampling times in the data sequence, and using the sum of all absolute differences as the result of negative correlation mapping as the primary stability.

[0036] Represented using a relational expression: , Indicates the primary stability of the data sequence. Indicates the length of the data sequence. Indicates the first An absolute difference, This represents an exponential function.

[0037] The accumulation of absolute differences reflects the severity of voltage changes; the larger the difference, the more significant the voltage fluctuation. Through exponential function mapping, these fluctuations can be transformed into a stability value, where smaller voltage fluctuations correspond to higher stability, thus effectively reflecting the stability level of the energy storage tank. In practical applications, this helps to monitor voltage stability in real time, promptly detect potential abnormal fluctuations, facilitate optimization of system operation and maintenance, and improve the reliability and safety of the energy storage tank.

[0038] It should be noted that the analysis of primary stability is based on the numerical variation characteristics of voltage data. However, in practical applications, voltage data often contains noise, and the values ​​of this noise data may be very close to those of normal voltage data, even making some originally significant voltage fluctuations less noticeable. Therefore, relying solely on the variation characteristics of voltage data to analyze primary stability may be affected by noise interference, failing to accurately reflect the true variation characteristics of the data sequence. The presence of noise can mask certain abnormal fluctuations, causing the analysis results to deviate from reality, thus affecting the accuracy of stability assessment.

[0039] During the discharge and charging processes of an energy storage tank, voltage and current data exhibit a correlation. During discharge, an increase in current leads to a decrease in voltage, showing a negative correlation between voltage and current; while during charging, an increase in current typically leads to an increase in voltage, showing a positive correlation.

[0040] To more accurately assess the stability of voltage data, it is necessary to consider the numerical variation characteristics of both voltage and current data. A strong correlation between voltage and current data indicates that voltage changes are more directly influenced by current changes, with less interference from noise. Therefore, analyzing the correlation between voltage and current can optimize the primary stability of the voltage data, making the analysis results more realistic and reliable. Conversely, a weak correlation between voltage and current data may indicate that voltage fluctuations are significantly affected by noise, resulting in lower reliability of the primary stability assessment. Therefore, correlation analysis of voltage and current data can effectively improve the accuracy and reliability of stability assessments.

[0041] Obtain all voltage extreme points in the data sequence and calculate the average sampling interval of the voltage extreme points. Similarly, obtain the average sampling interval of the current extreme points in the data sequence.

[0042] True stability satisfies the following relationship: , Indicates the true stability of the data sequence. Indicates the primary stability of the data sequence. This indicates the correlation between current and voltage curves in a data sequence. This represents the average sampling interval of voltage extreme points in the data sequence. This represents the average sampling interval of current extreme points in the data sequence. This represents the normalization function.

[0043] Extreme points in voltage and current reflect critical fluctuations in system operation. By measuring the intervals between these extreme points, the frequency and regularity of data fluctuations can be quantified, thereby effectively judging the dynamic characteristics of the system. By calculating a comprehensive index of primary stability, correlation, and extreme point intervals, the true stability of the data sequence can be more accurately assessed, thus providing more reliable stability analysis, reducing the impact of noise, and improving the actual prediction and control capabilities for the operational stability of energy storage tanks or other systems.

[0044] The correlation here refers to the Pearson correlation coefficient. The Pearson correlation coefficient ranges from -1 to 1, with its absolute value limiting the range to 0 to 1 to avoid the influence of charging and discharging. Therefore, the Pearson correlation coefficient clearly reveals the interaction between voltage and current, further aiding in optimizing stability assessment. When the Pearson correlation coefficient is close to 1, it indicates a strong positive correlation between voltage and current; voltage changes are significantly influenced by current changes, noise interference is low, and the reliability of primary stability is high. When the correlation coefficient is close to 0, it indicates that voltage and current changes are less correlated.

[0045] In addition, correlation can also be assessed using dynamic time warping. Using dynamic time warping to measure the correlation between voltage and current data, compared to the traditional Pearson correlation coefficient, can more effectively handle potential time delays or asynchrony between the two data points. In practical applications, changes in voltage and current data may not be strictly synchronized, especially in complex energy storage systems where voltage and current fluctuations may exhibit lag effects, or their fluctuation curves may not be perfectly aligned due to noise interference. Dynamic time warping, through nonlinear matching of two time series, can flexibly align these series, allowing for accurate assessment of their similarity and correlation even with temporal shifts in voltage and current signals. Dynamic time warping can eliminate errors caused by time delays or asymmetric changes, making stability analysis more precise.

[0046] In one embodiment, primary stability also includes:

[0047] Calculate the absolute difference of voltage between any two adjacent sampling times in the data sequence, calculate the cumulative value of all absolute differences, and use the product of the cumulative value and the voltage range in the data sequence through negative correlation mapping as the primary stability.

[0048] It can not only effectively capture the trend and degree of voltage fluctuations, but also help to better evaluate the dynamic stability of power systems and optimize equipment operation and management in practical applications by comprehensively considering the rate and magnitude of voltage changes.

[0049] S3. Calculate the sliding step size for each sampling time based on the true stability. Generate several sub-sequences by sliding the sliding window according to the sliding step size. Calculate the fuzzy entropy value. Obtain the adjacent fuzzy entropy value of the previous sampling time adjacent to the target point. Calculate the difference between the fuzzy entropy value of the target point and the adjacent fuzzy entropy value. Use the normalized difference as the anomaly degree of the target point to complete online monitoring.

[0050] In one embodiment, calculating the sliding step size includes: calculating the product of the true stability of the target point and the upper limit of the sliding step size, and using the sum of the product after rounding down and the lower limit of the sliding step size as the sliding step size of the target point.

[0051] It should be noted that when adaptively calculating the sliding window step size, a higher true stability of the data sequence corresponding to the voltage data point means smaller data fluctuations and smoother changes. Therefore, the sliding step size can be appropriately increased, which helps reduce the frequency of fuzzy membership degree calculations between subsequent subsequences and improves computational efficiency. In this way, unnecessary redundant calculations can be reduced, and processing speed can be optimized.

[0052] However, in order to ensure that each data point can be effectively divided into the corresponding subsequence, the size of the sliding step needs to be limited.

[0053] Specifically, the maximum step size should be the length of the subsequence to ensure the integrity of the subsequence and avoid excessive jumps between data points, while the minimum step size is 1 to ensure that the data sequence can be gradually and meticulously divided into multiple subsequences.

[0054] Therefore, by adaptively adjusting the step size, we can ensure the accuracy of the partitioning, improve computational efficiency, and optimize the system's processing performance in practical applications.

[0055] A special case may exist: after the number of sampling times at the end of the data sequence slides according to the adaptive sliding step size, some sampling times may not be included in the corresponding subsequences. In this case, these sampling times not included in the corresponding subsequences can be combined with several data points immediately preceding them in the time series to form a subsequence for subsequent analysis. The similarity tolerance for calculating the fuzzy membership degree between each subsequence within a sampling time's data sequence and other subsequences is preset to an empirical value of 0.5.

[0056] Several sub-sequences are generated by sliding a sliding window with a sliding step size. The fuzzy entropy value of the data sequence of the target point is calculated. The fuzzy entropy value of the data sequence corresponding to each sampling time is obtained by traversing. The adjacent fuzzy entropy values ​​of the previous sampling time adjacent to the target point are obtained. The difference between the fuzzy entropy value of the target point and the adjacent fuzzy entropy values ​​is calculated. The normalized difference is used as the anomaly degree of the target point. When the anomaly degree is greater than the preset anomaly threshold, it can be said that there is an anomaly in the voltage data of the target point, indicating that there may be an abnormal voltage change in the energy storage box. An alarm signal is generated and sent to notify relevant personnel to carry out maintenance.

[0057] This invention also discloses an online voltage monitoring system for an energy storage box, comprising a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an online voltage monitoring method for an energy storage box according to the present invention.

[0058] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0059] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device.

[0060] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

[0061] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for online voltage monitoring of an electrical energy storage box, characterized in that, include: The voltage and current data of the energy storage box are acquired according to the preset sampling interval, and then fitted into voltage curves and current curves respectively. Using any sampling time as the target point, construct the data sequence of the target point, calculate the primary stability of the data sequence, and calculate the true stability of the data sequence based on the primary stability, the correlation between the current curve and the voltage curve in the data sequence, the average sampling interval of the voltage extreme points, and the average sampling interval of the current extreme points. The sliding step size is calculated for each sampling time based on the true stability. Several subsequences are generated by sliding the sliding window according to the sliding step size. The fuzzy entropy value is calculated. The adjacent fuzzy entropy values ​​of the previous sampling time adjacent to the target point are obtained. The difference between the fuzzy entropy value of the target point and the adjacent fuzzy entropy values ​​is calculated. The normalized difference is used as the anomaly degree of the target point to complete the online monitoring. The calculation of the sliding step size includes: calculating the product of the true stability of the target point and the upper limit of the sliding step size, and using the sum of the product after rounding down and the lower limit of the sliding step size as the sliding step size of the target point.

2. The online voltage monitoring method for an electric energy storage box according to claim 1, characterized in that, The data sequence includes: A data sequence is constructed based on a preset number of historical sampling times, with the target point being the endpoint of the data sequence.

3. The online voltage monitoring method for an electric energy storage box according to claim 1, characterized in that, The primary stability includes: Calculate the absolute difference of voltage between any two adjacent sampling times in the data sequence, and use the sum of all absolute differences as the result of negative correlation mapping as the primary stability.

4. The online voltage monitoring method for an electric energy storage box according to claim 1, characterized in that, The primary stability also includes: Calculate the absolute difference of voltage between any two adjacent sampling times in the data sequence, calculate the cumulative value of all absolute differences, and use the product of the cumulative value and the voltage range in the data sequence through negative correlation mapping as the primary stability.

5. The online voltage monitoring method for an energy storage box according to claim 1, characterized in that, The true stability includes: Obtain all voltage extreme points in the data sequence and calculate the average sampling interval of the voltage extreme points. Similarly, obtain the average sampling interval of the current extreme points in the data sequence. True stability satisfies the following relationship: , Indicates the true stability of the data sequence. Indicates the primary stability of the data sequence. This indicates the correlation between current and voltage curves in a data sequence. This represents the average sampling interval of voltage extreme points in the data sequence. This represents the average sampling interval of current extreme points in the data sequence. This represents the normalization function.

6. The online voltage monitoring method for an electric energy storage box according to claim 5, characterized in that, The correlation is the Pearson correlation coefficient.

7. The online voltage monitoring method for an electric energy storage box according to claim 5, characterized in that, The correlation is dynamic time warping.

8. A voltage online monitoring system for an electrical energy storage box, characterized in that, include: Processor; and A memory storing computer instructions that, when executed by a processor, cause the system to perform a voltage online monitoring method for an energy storage box according to any one of claims 1-7.

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