Voltage online monitoring method and system for electric energy storage box

Abstract

The invention relates to the field of electric energy storage boxes, in particular to a voltage online monitoring method and system for an electric energy storage box. The method comprises the following steps: acquiring voltage data and current data of an electric energy storage box according to a preset sampling interval, and respectively fitting the voltage data and the current data into a voltage curve and a current curve; constructing a data sequence of the target point, calculating the primary stability of the data sequence, and calculating the real stability of the data sequence; and sliding the sliding window according to the calculated sliding step length to generate a plurality of subsequences, calculating a fuzzy entropy value, obtaining the anomaly degree of the target point, and completing online monitoring. According to the technical scheme of the invention, the efficiency and precision of the voltage monitoring result can be improved, and a guarantee is provided for the safe operation of the electric energy storage box.

Description

Technical Field

[0001] The present invention relates to the field of electric energy storage boxes. More specifically, the present invention relates to a method and system for online voltage monitoring of an electric energy storage box. Background Art

[0002] As a core component of an energy storage system, the performance of an energy storage box directly impacts the efficiency, safety, and lifespan of the entire system. To ensure efficient operation of the energy storage box, real-time voltage monitoring is crucial. Voltage monitoring not only helps assess battery health and promptly detect abnormal battery voltages, but also provides real-time operating data to the battery management system, enabling precise battery control. By monitoring voltage changes, the battery's charge and discharge processes can be effectively regulated, preventing overcharge or overdischarge and avoiding safety hazards caused by unstable voltage. Furthermore, voltage monitoring data can be used to predict battery lifespan, enabling timely maintenance and replacement, thereby extending the battery's 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 document with publication number CN119199379A discloses a single-phase grounding fault line selection method and system based on zero-sequence current modal decomposition. In this method, the IMF components of each line are linearly Gaussian fuzzy information granulated 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 fuzzy entropy difference between the voltage data sequence at the current moment and the previous moment, and the fixed sliding window step size will lead 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 leads to inaccurate voltage monitoring results. Summary of the Invention

[0005] In order to solve the problem of inaccurate voltage monitoring results, the present invention proposes a method and system for online voltage monitoring of an electric energy storage box.

[0006] In a first aspect, the present invention discloses a method for online voltage monitoring of an electric energy storage box, comprising: obtaining voltage data and current data of the electric energy storage box according to a preset sampling interval, and fitting them into a voltage curve and a current curve, 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 in the data sequence, and the average sampling interval; calculating the sliding step size at each sampling moment based on the true stability, generating a plurality of subsequences by sliding a sliding window based on the sliding step size, calculating a fuzzy entropy value, obtaining adjacent fuzzy entropy values at a 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 values, and using the normalized difference as the abnormality of the target point to complete the online monitoring; wherein, calculating the sliding step size comprises: 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 rounded-down product and the lower limit of the sliding step size as the sliding step size of the target point.

[0007] By calculating primary stability based on the data sequence at the target point and comprehensively considering the correlation between the current and voltage curves, the stability of the data sequence is evaluated, thereby achieving comprehensive control over the system's dynamic behavior. 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 adjacent points, anomalies in the energy storage box can be detected in real time, identifying potential faults or abnormal fluctuations.

[0008] Preferably, the data sequence includes: constructing a data sequence according to a preset number of historical sampling moments, and the target point is the end point of the data sequence.

[0009] Preferably, the primary stability comprises: calculating the absolute difference of the voltages at any two adjacent sampling moments in the data sequence, and using the accumulated value of all the absolute differences through negative correlation mapping as the primary stability.

[0010] The accumulation of absolute differences reflects the severity of voltage fluctuations; larger differences indicate more significant voltage fluctuations. These fluctuations can be converted into a stability value using an exponential function mapping, where smaller voltage fluctuations correspond to higher stability, effectively reflecting the stability level of the energy storage box.

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

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

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

[0014] By calculating the comprehensive indicators of primary stability, correlation, and extreme point intervals, the true stability of the data series can be evaluated more accurately, thereby providing a more reliable stability analysis, reducing the impact of noise, and improving the actual prediction and control capabilities of the operating stability of electric energy storage boxes 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 possible time delays or asynchrony issues between the two on the timeline.

[0018] In a second aspect, the present invention discloses an online voltage monitoring system for an electric energy storage box, comprising: a processor; and a memory, wherein the memory stores computer instructions. When the computer instructions are executed by the processor, the system executes the above-mentioned online voltage monitoring method for an electric energy storage box.

[0019] Beneficial effects of the present invention: 1. By analyzing the changing characteristics of voltage data and current data, the present 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.

[0020] 2. This invention dynamically adjusts the sliding window step size based on the data stability index, allowing for flexible adjustment of computational density within different stability regions. Specifically, regions with higher stability can use a larger sliding window step size, thereby reducing the number of fuzzy membership calculations between subsequent subsequences and lowering the computational effort. In regions with greater fluctuations, a smaller sliding window step size ensures detection accuracy and avoids false detections.

[0021] 3. Analyzing the changing characteristics of voltage and current data effectively reduces noise interference, especially in the presence of external interference such as electromagnetic waves. When the noise is close to the normal data value, the optimization scheme can more accurately adjust the sliding step size, avoiding calculation errors caused by noise. This not only improves computational efficiency but also maintains the accuracy of anomaly detection, ensuring the system's responsiveness and accuracy during real-time monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 This is a flow chart of a method for online voltage monitoring of an electric energy storage box according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first," "second," and the like are used in the claims, description, and drawings of the present invention, they are merely used to distinguish between different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the description and claims of the present invention indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0025] The present invention provides a method for online voltage monitoring of an electric energy storage box. Figure 1 As shown, a method for online voltage monitoring of an electric energy storage box includes steps S1 to S3, which are described in detail below.

[0026] S1, obtaining voltage data and current data of the electric energy storage box according to a preset sampling interval, and fitting them into a voltage curve and a current curve respectively.

[0027] In one embodiment, in order to accurately monitor the voltage of the electric energy storage box, first, the voltage and current data are collected by the voltage sensor and the current sensor respectively, and it is ensured that the collection of the two is synchronous, that is, started and ended at the same time.

[0028] Exemplarily, the acquisition 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.

[0029] Analog-to-digital conversion equipment is used to convert the collected analog signals into digital signals, obtaining digital representations of the voltage and current data. Existing curve fitting techniques are then used to fit these digitized voltage and current data to generate smooth voltage and current curves. These curves more clearly reflect the operating status of the energy storage box, providing a basis for subsequent data analysis and fault diagnosis.

[0030] S2, taking any sampling moment as the target point, constructing the 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 in the data sequence, and the average sampling interval.

[0031] In one embodiment, the process of constructing a data sequence is to select a preset number of historical sampling moments to form a data sequence containing a target point, where the target point is the end point of the sequence. For example, the preset number is 200, i.e., 200 historical sampling moments before the target point are selected to construct the data sequence.

[0032] If there are fewer than 200 historical sampling times before the target point, additional data within 1 minute before the target point is collected to ensure the integrity of the data sequence. It is important to note that this additional 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 is always consistent and does not interfere with anomaly detection.

[0033] Calculating the primary stability includes: calculating the absolute difference between the voltages at any two adjacent sampling moments in the data sequence, and using the accumulated value of all the absolute differences through negative correlation mapping as the primary stability.

[0034] The relationship is expressed as: , Indicates the primary stability of the data series, Indicates the length of the data sequence, Indicates the The absolute difference, Represents the exponential function.

[0035] The accumulation of absolute differences reflects the severity of voltage fluctuations; larger differences indicate more significant voltage fluctuations. Using exponential function mapping, these fluctuations can be converted into a stability value, where smaller voltage fluctuations correspond to higher stability, effectively reflecting the stability level of the energy storage box. In practical applications, this can help monitor voltage stability in real time, promptly identify potential abnormal fluctuations, facilitate optimization of system operation and maintenance, and improve the reliability and safety of the energy storage box.

[0036] It's important to note that primary stability analysis is based on the numerical variation characteristics of voltage data. However, in practical applications, voltage data is often accompanied by a certain amount of noise. The values of this noise data can be very close to normal voltage data, even causing some previously significant voltage fluctuations to become less noticeable. Therefore, relying solely on the variation characteristics of voltage data to analyze primary stability may be affected by noise and fail to accurately reflect the true variation characteristics of the data series. The presence of noise can mask some abnormal fluctuations, causing the analysis results to deviate from the actual situation, thereby affecting the accuracy of the stability assessment.

[0037] During the discharge and charging process of an energy storage tank, a correlation is observed between voltage and current data. During discharge, an increase in current causes a decrease in voltage, resulting in a negative correlation between voltage and current. During charging, an increase in current typically causes an increase in voltage, resulting in a positive correlation.

[0038] To more accurately assess the stability of voltage data, it's necessary to consider the numerical variation characteristics of both voltage and current data. A strong correlation between voltage and current data indicates that voltage fluctuations are more directly affected by current fluctuations, with less interference from noise data. Therefore, by analyzing the correlation between voltage and current, we can optimize the primary stability of the voltage data, making the analysis more reliable. A weak correlation between voltage and current data may indicate that voltage fluctuations are significantly affected by noise, making the primary stability assessment less reliable. Therefore, analyzing the correlation between voltage and current data can effectively improve the accuracy and credibility of stability assessments.

[0039] Obtain all voltage extreme value points in the data sequence, calculate the average sampling interval of the voltage extreme value points, and similarly obtain the average sampling interval of the current extreme value points in the data sequence.

[0040] The true stability satisfies the relationship: , Indicates the true stability of the data series, Indicates the primary stability of the data series, Indicates the correlation between the current curve and the voltage curve in the data sequence, represents the average sampling interval of the voltage extreme points in the data sequence, represents the average sampling interval of the current extreme points in the data sequence, Represents the normalization function.

[0041] The extreme points of voltage and current reflect key fluctuations in system operation. By measuring the intervals between these extreme points, the frequency and regularity of data fluctuations can be quantified, effectively determining the system's dynamic characteristics. By calculating a comprehensive indicator of primary stability, correlation, and the intervals between extreme points, the true stability of the data series can be more accurately assessed, providing more reliable stability analysis, reducing the impact of noise, and improving the ability to predict and control the operational stability of energy storage boxes or other systems.

[0042] The correlation is the Pearson correlation coefficient. The Pearson correlation coefficient ranges from -1 to 1, and the absolute value is used to limit the range to between 0 and 1 to avoid the influence of charging and discharging. Therefore, the Pearson correlation coefficient can clearly reveal the interaction between voltage and current, further helping to optimize stability assessment. When the Pearson correlation coefficient is close to 1, it means that there is a strong positive correlation between voltage and current, the voltage change is more directly affected by the current change, the noise interference is small, and the credibility of the primary stability is high. When the correlation coefficient is close to 0, it means that the changes in voltage and current are less correlated.

[0043] In addition, dynamic time warping can also be used for correlation. Using dynamic time warping to measure the correlation between voltage and current data can more effectively deal with the time delay or asynchrony problems that may exist between the two on the time axis compared to the traditional Pearson correlation coefficient. In practical applications, the changes in voltage and current data may not be strictly synchronized. Especially in complex electric energy storage systems, the fluctuations of voltage and current may have a lag effect, or due to noise interference, the fluctuation curves of the two may not be completely aligned. Dynamic time warping can flexibly align these sequences by performing nonlinear matching on two time series, so that even if the voltage and current signals are offset in time, the similarity and correlation between them can still be accurately evaluated. Dynamic time warping can eliminate errors caused by time delays or asymmetric changes, making stability analysis more accurate.

[0044] In one embodiment, the primary stability further comprises: The absolute difference of the voltage between any two adjacent sampling moments in the data sequence is calculated, the cumulative value of all absolute differences is calculated, and the product of the cumulative value and the voltage range in the data sequence is negatively mapped as the primary stability level.

[0045] It can not only effectively capture the trend and degree of voltage fluctuations, but also help better evaluate the dynamic stability of the power system and optimize the operation and management of equipment in practical applications by comprehensively considering the rate and amplitude of voltage change.

[0046] S3, calculate the sliding step size at each sampling moment based on the true stability, generate several subsequences based on the sliding step size sliding window, calculate the fuzzy entropy value, obtain the adjacent fuzzy entropy value of the previous sampling moment adjacent to the target point, calculate the difference between the fuzzy entropy value of the target point and the adjacent fuzzy entropy value, and use the normalized difference as the abnormality of the target point to complete the online monitoring.

[0047] In one embodiment, calculating the sliding step includes: calculating the product of the true stability of the target point and the upper limit of the sliding step, and taking the sum of the rounded-down product and the lower limit of the sliding step as the sliding step of the target point.

[0048] It should be noted that when adaptively calculating the sliding window step size, the greater the true stability of the data sequence corresponding to the voltage data point, the smaller the data fluctuation and the smoother the change. Therefore, the sliding step size can be appropriately increased. This helps reduce the frequency of fuzzy membership calculations between subsequent subsequences and improves computational efficiency. In this way, unnecessary repeated calculations can be reduced and processing speed can be optimized.

[0049] 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 subject to certain restrictions.

[0050] Specifically, the maximum value of the 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 value of the step size is 1 to ensure that the data sequence can be divided into multiple subsequences step by step and in detail.

[0051] Therefore, by adaptively adjusting the step size, we can not only ensure the accuracy of division, but also improve the computing efficiency and optimize the processing performance of the system in practical applications.

[0052] A special case may occur: after the number of sampling moments at the end of a data sequence slides according to the adaptive sliding step size, some sampling moments may not be divided into corresponding subsequences. In this case, these sampling moments that are not divided into corresponding subsequences can be combined with several data points immediately before them in the time series to form a subsequence for subsequent analysis. The similarity tolerance for calculating the fuzzy membership of each subsequence with other subsequences within a data sequence of a sampling moment is preset to an empirical value of 0.5.

[0053] According to the sliding step size, a sliding window is used to generate several subsequences, and 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 moment is traversed to obtain the adjacent fuzzy entropy value of the previous sampling moment adjacent to the target point. The difference between the fuzzy entropy value of the target point and the adjacent fuzzy entropy value is calculated, and the normalized difference is used as the abnormality of the target point. When the abnormality is greater than the preset abnormality threshold, it can be said that there is an abnormality in the voltage data of the target point, indicating that abnormal voltage changes may have occurred in the electric energy storage box. An alarm signal is generated and sent to notify relevant personnel to carry out maintenance.

[0054] An embodiment of the present invention also discloses an online voltage monitoring system for an electric energy storage box, comprising a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, an online voltage monitoring method for an electric energy storage box according to the present invention is implemented.

[0055] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0056] In the present invention, the aforementioned memory may be any tangible medium that contains or stores 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 may be any suitable magnetic storage medium 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), or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium may be part of, accessible to, or connectable to a device.

[0057] While this specification has shown and described several embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0058] 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, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for online voltage monitoring of an electric energy storage box, characterized in that: include: Acquire voltage data and current data of the electric energy storage box according to a preset sampling interval, and fit them into a voltage curve and a current curve respectively; Taking any sampling moment 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; The sliding step length at each sampling moment is calculated based on the true stability. A sliding window is then slid based on the sliding step length to generate several subsequences. The fuzzy entropy value is calculated, and the adjacent fuzzy entropy value of the previous sampling moment adjacent to the target point is obtained. The difference between the fuzzy entropy value of the target point and the adjacent fuzzy entropy value is calculated. The normalized difference is used as the abnormality degree of the target point to complete the online monitoring. The calculation of the sliding step includes: calculating the product of the true stability of the target point and the upper limit of the sliding step, and taking the sum of the rounded-down product and the lower limit of the sliding step as the sliding step of the target point.

2. A method for online voltage monitoring of 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 moments, and the target point is the end point of the data sequence.

3. The method for online voltage monitoring of an electric energy storage box according to claim 1, wherein: The primary stability includes: The absolute difference of the voltage between any two adjacent sampling moments in the data sequence is calculated, and the cumulative value of all absolute differences is used as the result of negative correlation mapping as the primary stability level.

4. A method for online voltage monitoring of an electric energy storage box according to claim 1, characterized in that: The primary stability also includes: The absolute difference of the voltage between any two adjacent sampling moments in the data sequence is calculated, the cumulative value of all absolute differences is calculated, and the product of the cumulative value and the voltage range in the data sequence is negatively mapped as the primary stability level.

5. The method for online voltage monitoring of an electric energy storage box according to claim 1, wherein: The true stability includes: Obtain all voltage extreme value points in the data sequence, calculate the average sampling interval of the voltage extreme value points, and similarly obtain the average sampling interval of the current extreme value points in the data sequence; The true stability satisfies the relationship: , Indicates the true stability of the data series, Indicates the primary stability of the data series, Indicates the correlation between the current curve and the voltage curve in the data sequence, represents the average sampling interval of the voltage extreme points in the data sequence, represents the average sampling interval of the current extreme value points in the data sequence, Represents the normalization function.

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

7. The method for online voltage monitoring of 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 electric energy storage box, characterized in that: include: processor; and A memory storing computer instructions, wherein when the computer instructions are executed by a processor, the system executes a method for online voltage monitoring of an electric energy storage box according to any one of claims 1 to 7.

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