A method and system for suppressing voltage fluctuations in mobile modular energy storage

Abstract

This application relates to the field of voltage fluctuation suppression technology, specifically to a method and system for suppressing voltage fluctuations in mobile modular energy storage. The method includes: analyzing load current and voltage data collected by the mobile modular energy storage at adjacent temperature sampling times to obtain load current and voltage characteristic values; analyzing the similarity in distribution between temperature data, load current characteristic values, and voltage characteristic values; combining the differences in the degree of disorder between temperature data, load current characteristic values, and voltage characteristic values ​​to obtain temperature influence weights and load current influence weights; weighting the changes in temperature and load current in the nearest neighbor set to determine dynamic influence indicators; and adjusting the domain of fuzzy PI control to suppress voltage fluctuations. This application aims to improve the voltage fluctuation suppression effect of mobile modular energy storage and ensure power supply stability.

Description

Technical Field

[0001] This application relates to the field of voltage fluctuation suppression technology, specifically to a method and system for suppressing voltage fluctuations in mobile modular energy storage. Background Technology

[0002] With the continuous development of energy storage and power electronics technologies, mobile modular energy storage offers a novel solution for addressing seasonal fluctuations in electricity demand. Through real-time charge and discharge regulation, mobile modular energy storage can effectively mitigate voltage fluctuations at the end of the grid caused by seasonal or periodic load surges, ensuring that user voltage remains within a stable range. Simultaneously, when dealing with grid instability issues caused by renewable energy fluctuations and load changes, mobile modular energy storage can guarantee power quality, protect the safe operation of equipment, and improve energy efficiency. Therefore, mobile modular energy storage provides an efficient and flexible approach to addressing seasonal changes in electricity demand.

[0003] When using mobile modular energy storage for supplemental power supply, external temperature and load current variations can cause voltage fluctuations, which can affect the safe operation and lifespan of the equipment. Therefore, voltage fluctuation suppression is necessary during the operation of mobile modular energy storage. When addressing voltage fluctuation suppression for mobile modular energy storage, it's crucial to consider scenarios where this system is commonly used for temporary power supply, emergency power, and microgrids. In these environments, load current may fluctuate significantly within a short period, and temperature variations under different conditions can also impact the system. These factors can lead to transient fluctuations in bus voltage, such as voltage dips or rises, thereby affecting the controller's real-time monitoring and judgment of the system status, resulting in inadequate voltage fluctuation suppression. Summary of the Invention

[0004] In view of the above, it is necessary to provide a method and system for suppressing voltage fluctuations in mobile modular energy storage to solve the above problems.

[0005] The first aspect of this application provides a method for suppressing voltage fluctuations in mobile modular energy storage, the method comprising:

[0006] The load current and voltage data collected at adjacent temperature sampling times by the mobile module energy storage are analyzed to obtain the characteristic values ​​of the load current and voltage.

[0007] A nearest neighbor set is pre-defined for each temperature sampling time. The distribution similarity characteristics of temperature data, load current characteristic value and voltage characteristic value are analyzed. Combined with the difference in disorder between temperature data, load current characteristic value and voltage characteristic value, the influence weight of temperature and the influence weight of load current are obtained for each temperature sampling time. The changes in temperature and load current in the nearest neighbor set are weighted respectively to determine the dynamic influence index for each temperature sampling time.

[0008] Based on the dynamic impact index at each temperature sampling time, the domain of fuzzy PI control is adjusted to suppress voltage fluctuations.

[0009] The load current characteristic value is obtained by averaging all load current data between adjacent temperature sampling times; the voltage characteristic value is obtained by averaging all voltage data between adjacent temperature sampling times.

[0010] The specific process for obtaining the temperature influence weight and load current influence weight at each temperature sampling time is as follows:

[0011] For each temperature sampling time, the nearest neighbor set is used to determine the first correlation and the second correlation based on the distribution similarity characteristics.

[0012] Analyze the coefficients of variation of all first correlations and all second correlations obtained from the nearest neighbor sampling set at each temperature sampling time to determine the first degree of dispersion and the second degree of dispersion;

[0013] Based on the degree of disorder of each data type in the nearest neighbor set at each temperature sampling time, the characteristics of each data type are determined;

[0014] Analyze the distance distribution of temperature data, load current characteristic value and voltage characteristic value in the nearest neighbor set at each temperature sampling time, and combine the first degree of dispersion and the second degree of dispersion to obtain the first influence feature and the second influence feature;

[0015] The proportion of the first influence feature of voltage at each temperature sampling moment among all influence features is denoted as the temperature influence weight; the proportion of the second influence feature of voltage at each temperature sampling moment among all influence features is denoted as the load current influence weight.

[0016] The distribution similarity characteristics are determined using the Pearson correlation coefficient.

[0017] The process for obtaining each data feature is as follows:

[0018] Obtain the sample entropy and permutation entropy of each data type in the nearest neighbor set at each temperature sampling time, and use the product of the sample entropy and permutation entropy as the feature of each data type at each temperature sampling time.

[0019] Specifically, obtaining the first influence feature and the second influence feature involves:

[0020] For each temperature sampling moment, calculate the DTW distance between the sequence of all load current characteristics and the sequence of all voltage characteristics in the nearest neighbor set of each temperature sampling moment, and denote it as the first feature index of the voltage characteristics at each temperature sampling moment; based on the first feature index and the first degree of dispersion, obtain the first influence feature of the voltage at each temperature sampling moment.

[0021] Calculate the DTW distance between the sequence composed of all temperature features and the sequence composed of all voltage features, and denote it as the second feature index of the voltage feature at each temperature sampling time; based on the second feature index and the second degree of dispersion, obtain the second influence feature of the voltage at each temperature sampling time.

[0022] Specifically, the first influencing feature is the ratio of a first feature index to a first degree of dispersion; the second influencing feature is the ratio of a second feature index to a second degree of dispersion.

[0023] Specifically, determining the dynamic influence index for each temperature sampling moment includes:

[0024] For each temperature sampling time, the nearest neighbor set is used to obtain the average temperature and the average load current characteristic value.

[0025] Calculate the difference between the temperature data at each temperature sampling time and the previous temperature data, and denote it as the first difference; calculate the difference between the load current characteristic value at each temperature sampling time and the previous load current characteristic value, and denote it as the second difference;

[0026] Calculate the ratio of the first difference to the average temperature, and record it as the first ratio; calculate the ratio of the second difference to the average load current characteristic value, and record it as the second ratio;

[0027] The temperature influence weight is used as the weight of the first ratio, and the load current influence weight is used as the weight of the second ratio. The weighted sum is then normalized to obtain the dynamic influence index for each temperature sampling time.

[0028] Specifically, adjusting the universe of discourse for fuzzy PI control involves calculating the sum of the natural number 1 and the dynamic influence index corresponding to each temperature acquisition moment, and then multiplying it by the preset universe of discourse to obtain the dynamically adjusted universe of discourse for each temperature acquisition moment.

[0029] Secondly, embodiments of this application also provide a voltage fluctuation suppression system for mobile modular energy storage, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0030] This application has at least the following beneficial effects:

[0031] This application first analyzes the load current and voltage data collected at adjacent temperature sampling times to obtain the characteristic values ​​of load current and voltage. By calculating the characteristic values ​​of load current and voltage, accurate quantitative indicators of the system's power consumption and voltage changes can be obtained, which helps in subsequent modeling and control of the dynamic impact of these variables and provides data support for control strategies. A nearest neighbor set is pre-defined for each temperature sampling time. By analyzing the similarity of the distribution of temperature data, load current characteristic values, and voltage characteristic values, the stability and consistency of the system under different conditions can be revealed. If these data show similar distribution characteristics in time or space, it indicates that they may have some inherent regularity, which can be used to optimize control strategies. Furthermore, analyzing the degree of disorder between them and quantifying the interference and complexity between different factors helps identify more sensitive or volatile parts of the system, thus providing a basis for subsequent weighting and dynamic adjustment. Combining the differences between temperature data, load current characteristic values, and voltage characteristic values, the temperature influence weight and load current influence weight at each temperature sampling time are calculated. The weights reflect the contribution of each factor to voltage fluctuations, allowing for more precise quantification of the specific impact of different factors (such as temperature and load current) on voltage fluctuations. This enables targeted adjustments to the system. If a factor (such as temperature) has a significant impact on voltage fluctuations, it can be assigned a higher weight, thereby improving control accuracy and response speed. Weighting the temperature and load current changes in the nearest neighbor set yields a dynamic impact index for each temperature sampling moment. This weighting process integrates temperature and load current changes at different times or locations, balancing the influence of different factors and preventing excessive influence from any single factor that could lead to control imbalance. The dynamic impact index provides a comprehensive indicator that allows for real-time adjustment of the system's control strategy, optimizing voltage fluctuation suppression. Based on the dynamic impact index for each temperature sampling moment, the universe of discourse of the fuzzy PI control is adjusted. By dynamically adjusting the universe of discourse of the fuzzy PI controller, the system's response can be controlled more precisely. This dynamic adjustment enables the controller to adapt to complex and changing environments, suppressing voltage fluctuations while avoiding unnecessary overreaction. Attached Figure Description

[0032] Figure 1 A flowchart illustrating the steps of a method for suppressing voltage fluctuations in mobile modular energy storage, provided in one embodiment of this application;

[0033] Figure 2 This is a flowchart illustrating the acquisition of temperature influence weight and load current influence weight according to one embodiment of this application. Detailed Implementation

[0034] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0035] Unless otherwise defined, all technical and scientific terms used herein 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 application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0036] It should also be noted that the terms "first" and "second" in this application and its accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged, and some steps can also be deleted.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0038] The following description, in conjunction with the accompanying drawings, details the specific scheme of the voltage fluctuation suppression method and system for mobile modular energy storage provided in this application.

[0039] Please see Figure 1 The diagram illustrates a flowchart of a voltage fluctuation suppression method for mobile module energy storage according to an embodiment of this application. The method includes the following steps:

[0040] The first step is to analyze the load current and voltage data collected at adjacent temperature sampling times of the mobile energy storage module to obtain the load current characteristic value and voltage characteristic value.

[0041] When suppressing voltage fluctuations in mobile modular energy storage, a wireless temperature sensor is used to acquire real-time temperature data of the mobile modular energy storage during operation. The temperature data acquired is from the heat sink of the mobile modular energy storage, and the temperature data is collected once per second, i.e., the sampling frequency is set to 1Hz to ensure that the data interval is not too large and will cause abnormal fluctuations. A Hall effect current sensor is used to acquire real-time load current data of the mobile modular energy storage during operation, and the sampling frequency is set to 50KHz. A voltage sensor is placed at the output end of the mobile modular energy storage to acquire voltage data in real-time, and the sampling frequency is set to 50KHz. When acquiring all data, a protocol time synchronization method (GPTP protocol) is used to unify the acquisition of signal data from all sensors to the UTC+8 time standard to avoid signal time mismatch. For temperature data, load current data, and voltage data, corresponding data from one week in the past are obtained for comparative analysis.

[0042] Next, the acquired temperature, current, and voltage data are transmitted in real time to the terminal processing module via the data transmission module using the CAN bus. The terminal processing module performs preprocessing operations on the acquired signals, taking the real-time acquired temperature, current, and voltage data as inputs and using a Gaussian filtering algorithm for filtering and denoising, and outputting the denoised signal data. The Gaussian filtering algorithm is a well-known technology in this field, and the specific operation process will not be described in detail.

[0043] When handling voltage fluctuation suppression in mobile modular energy storage, it is usually necessary to combine a fuzzy PI controller with droop control to form a dual closed-loop droop control of voltage and current to suppress voltage fluctuations in real time. Considering that mobile energy storage systems often serve temporary power supply, emergency power supply, microgrid and other scenarios, the load may change significantly in a short period of time. At the same time, temperature changes may occur in different scenarios. High temperature will reduce the internal resistance of the energy storage battery, while low temperature will increase its internal resistance. All of these will cause transient drops or rises in the bus voltage.

[0044] In using a fuzzy PI controller, to cope with the influence of interference factors, the domain of discourse of the PI controller needs to be dynamically adjusted to adapt to changes in the system in real time. The specific operation process is as follows:

[0045] Considering that when using mobile modular energy storage for power supply, the temperature of the energy storage device fluctuates due to different environments and load conditions. Under different temperature changes, the internal resistance of the energy storage device usually changes, affecting voltage stability. Simultaneously, changes in load current also significantly impact voltage stability, and there is often coupling interference between these two factors. For example, at low temperatures, the battery's internal resistance increases, resulting in a greater magnitude and speed of voltage drop when dealing with the same sudden load increase. Therefore, this study first analyzes the temperature and load signals. Since the temperature sampling frequency is 1Hz, while the current and voltage sampling frequencies are both 50kHz, to achieve time synchronization of temperature, voltage, and current, the instantaneous voltage value needs to be converted into a characteristic value. The load current and voltage data within the sampling interval between each temperature data point and the next are averaged to obtain the characteristic values ​​of the load current and voltage at each moment. The load current characteristic value and voltage characteristic value are analyzed in subsequent analyses. Thus, each temperature data sampling time corresponds to a temperature data, a load current characteristic value and a voltage characteristic value. For ease of subsequent description, the sampling time corresponding to each temperature data is referred to as the temperature sampling time.

[0046] The second step is to pre-determine the nearest neighbor set for each temperature sampling moment, analyze the similarity characteristics of the distribution of temperature data, load current characteristic values ​​and voltage characteristic values, and combine the differences in the degree of disorder between temperature data, load current characteristic values ​​and voltage characteristic values ​​to obtain the temperature influence weight and load current influence weight for each temperature sampling moment. The changes in temperature and load current in the nearest neighbor set are weighted separately to determine the dynamic influence index for each temperature sampling moment.

[0047] This application uses the set of each temperature sampling moment and the previous k temperature sampling moments as the nearest neighbor set of the temperature data at each temperature sampling moment, which is used to analyze the temperature change characteristics of the local time interval up to the current temperature sampling moment. For the load current characteristic value and the voltage characteristic value, the nearest neighbor set corresponding to the temperature is also obtained. In this embodiment, k is taken as 60. If there are less than k temperature data sampling moments before the current temperature sampling moment, all previous temperature data sampling moments are selected as their corresponding nearest neighbor set.

[0048] Considering that increased or decreased internal power under increased load generates different amounts of heat, affecting the temperature of energy storage devices, and that the external environment also influences the temperature, as temperature changes, lower temperatures lead to increased resistance and a relative decrease in voltage. As temperature increases, the voltage gradually recovers, but at a certain temperature, it affects component performance, causing the voltage to weaken again. Fluctuations in load current also cause related voltage fluctuations, exhibiting a negative correlation. In the absence of temperature changes, the changes in voltage and load current are inversely similar. However, under severe temperature influences, the effect of load current on voltage shifts. Therefore, at the same temperature sampling time, voltage changes will be affected to varying degrees by temperature and load current.

[0049] Analyze the impact of temperature data and load current data on voltage at the current temperature sampling time: Within the nearest neighbor set at each temperature sampling time, the sequence of all temperature data is taken as the temperature sequence. Similarly, for the nearest neighbor set corresponding to each load current characteristic value and voltage characteristic value, the load current characteristic value sequence and voltage characteristic value sequence are obtained respectively.

[0050] First, obtain the absolute value of the Pearson correlation coefficient between the temperature sequence and the voltage feature value sequence corresponding to each temperature sampling time, and use it as the first correlation coefficient, denoted as | |;Obtain the absolute value of the Pearson correlation coefficient between the load current characteristic value sequence and the voltage characteristic value sequence corresponding to each temperature sampling time, and use it as the second correlation, denoted as | |;For each temperature sampling moment, the two correlations corresponding to each sampling point in the nearest neighbor set| | and | To determine the stability of the effects of temperature and load current on voltage, based on the above analysis, a comprehensive analysis is performed on the correlation of all sampling points in the nearest neighbor set corresponding to each temperature sampling time. Specifically, the coefficient of variation of all first correlations obtained in the nearest neighbor set at each temperature sampling time is calculated, and this coefficient is used as the first degree of dispersion, denoted as . Calculate the coefficient of variation of all second correlations obtained in the nearest neighbor set at each temperature sampling time, and use this as the second degree of dispersion, denoted as . The first correlation and the second correlation represent the correlation characteristics between temperature and voltage characteristic values ​​and between load current characteristic values ​​and voltage characteristic values, respectively, for all sampling points in the nearest neighbor set at each temperature sampling time. When the first dispersion is greater, it indicates that the correlation between temperature and voltage characteristic values ​​is more unstable and the influence of temperature is more unstable. When the second dispersion is greater, it indicates that the correlation between load current characteristic values ​​and voltage characteristic values ​​is more unstable and the influence of load current characteristic values ​​is more unstable.

[0051] Furthermore, under the influence of temperature, the correlation between voltage fluctuations and load current fluctuations shifts, and changes in load current also alter battery energy consumption and heat generation. Therefore, the sample entropy and permutation entropy of the temperature sequence at each temperature sampling moment are obtained to measure the disorder of the temperature sequence elements, and the product of the sample entropy and permutation entropy is used as the temperature feature at each temperature sampling moment. On the other hand, the temperature sequence is replaced with load current feature value sequences and voltage feature value sequences respectively, and the load current feature and voltage feature at each temperature sampling moment are calculated in the same way. The temperature feature, load current feature, and voltage feature are all recorded as data features.

[0052] Because temperature changes resistance, the impact of load current on voltage also changes. The sample entropy and permutation entropy of the load current's nearest neighbor sampling points differ significantly from those of the corresponding voltage's nearest neighbor sampling points. Based on this analysis, a comprehensive analysis of the current and voltage characteristics under temperature influence is conducted: For each temperature sampling moment, the DTW distance between the sequences of all load current characteristics and the sequences of all voltage characteristics in the nearest neighbor set is calculated. This distance serves as the characteristic index between load current and voltage characteristics under temperature influence and is denoted as the first characteristic index of the voltage characteristic at each temperature sampling moment. It should be noted that when the temperature influence is small, the sample entropy and permutation entropy of the obtained load current and voltage are relatively close, resulting in a smaller calculated DTW distance. However, when the temperature influence is severe, the characteristic relationship between load current and voltage changes under temperature interference, leading to differences in the calculated sample entropy and permutation entropy. This results in differences in the current and voltage characteristics corresponding to the sampling points, and a larger calculated DTW distance. The same method is used to obtain the characteristic index between temperature and voltage characteristics under the influence of load current, which is denoted as the second characteristic index of voltage characteristics at each temperature sampling time.

[0053] Considering the stability of the influence of temperature and load current on voltage, this application takes temperature as an example. The more stable the temperature influence, the more dominant the current influence on voltage, and the higher the reliability of its influence. Therefore, the ratio of the first characteristic index to the first degree of dispersion is calculated to obtain the first influence feature of voltage at each temperature sampling time, used to represent the influence feature of temperature on voltage. It should be noted that to prevent the denominator from being zero, a preset parameter greater than zero and not zero needs to be added to the denominator. In this embodiment, the parameter value is 0.01. The first characteristic index is replaced with the second characteristic index, and the first degree of dispersion is replaced with the second degree of dispersion. Using the same acquisition method as the first influence feature, the second influence feature of voltage at each temperature sampling time is obtained, used to represent the influence feature of load current on voltage. The percentage of the first influence feature of voltage at each temperature sampling time among all influence features is recorded as the temperature influence weight; the percentage of the second influence feature of voltage at each temperature sampling time among all influence features is recorded as the load current influence weight. The flowcharts for obtaining the temperature influence weight and the load current influence weight are as follows: Figure 2 As shown.

[0054] It should be understood that when the voltage is more severely affected by temperature and remains stably affected by it, the smaller the calculated first degree of dispersion and the larger the first characteristic index, the greater the obtained weight of temperature influence; when the voltage is more severely affected by current and remains stably affected by it, the smaller the calculated second degree of dispersion and the larger the value of the second characteristic index, the greater the obtained weight of load current influence.

[0055] Based on the acquired influence weights, a dynamic influence index is constructed to characterize the combined influence of temperature and load current on voltage at each temperature sampling time. Specifically, for the nearest neighbor set at each temperature sampling time, the mean temperature and the mean load current characteristic value are obtained; the difference between the temperature data at each temperature sampling time and the previous temperature data is calculated and denoted as the first difference; the difference between the load current characteristic value at each temperature sampling time and the previous load current characteristic value is calculated and denoted as the second difference; the ratio of the first difference to the mean temperature is calculated and denoted as the first ratio; the ratio of the second difference to the mean load current characteristic value is calculated and denoted as the second ratio; the temperature influence weight is used as the weight of the first ratio, and the load current influence weight is used as the weight of the second ratio, and the weighted sum is performed and normalized to obtain the dynamic influence index at each temperature sampling time.

[0056] In this embodiment, the differences between variables are calculated using the absolute value of the difference, and the sigmoid function is used as the normalization function. The specific formula is as follows: In the formula, This is the dynamic impact index at the a-th temperature sampling time, used to represent the intensity of external disturbances caused by temperature and load current. For normalization function, This represents the temperature influence weight corresponding to the a-th temperature sampling time. This represents the load current influence weight at the a-th temperature sampling time. This represents the first difference at the a-th temperature sampling time. This represents the second difference at the a-th temperature sampling time. , Let represent the mean temperature and the mean load current characteristic value in the nearest neighbor set at the a-th temperature sampling time, respectively; where, Indicates the first ratio. This indicates the second ratio.

[0057] It should be understood that when existing technologies combine temperature changes and current changes to analyze the comprehensive impact of voltage, they do not take into account that the effects of temperature and load current change in real time under actual conditions. This can lead to abnormal deviations in the control amplitude when suppressing voltage fluctuations in mobile module energy storage. Therefore, this application combines the influence of temperature and load current to perform feature weighting to obtain the real-time impact, thereby reducing the possibility of abnormal deviations in voltage fluctuation suppression.

[0058] The third step is to adjust the domain of the fuzzy PI control based on the dynamic impact index at each temperature sampling time to suppress voltage fluctuations.

[0059] Dynamic influence indices reflect the combined impact of temperature and load current on voltage. To ensure sufficient adjustment range when using a fuzzy PI controller to control voltage, especially in the face of voltage fluctuations caused by temperature and load current changes, the universe of discourse (UD) of the fuzzy PI controller is adjusted using dynamic influence indices. Taking each temperature sampling moment as an example, the adjusted UD for each temperature sampling moment is obtained. Specifically, the sum of the natural number 1 and the dynamic influence indices corresponding to each temperature sampling moment is calculated, and then multiplied by the preset UD to obtain the dynamically adjusted UD for each temperature sampling moment. In this embodiment, the initial UD of the fuzzy PI controller is... Quantization factors are mapped to fuzzy sets as follows: That is, it corresponds to {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. Since the universe of discourse refers to the range of all possible values ​​of the fuzzy variable, it does not need to be restricted to integers.

[0060] It should be understood that existing technologies typically use empirical settings to configure fuzzy subsets, but fail to consider the random variations in voltage fluctuations caused by temperature and load current changes during actual processing. This results in insufficient adjustment range when using a fuzzy PI controller to suppress large voltage fluctuations. Therefore, based on the aforementioned prior knowledge, this application fully incorporates the degree of temperature and load current interference experienced by the corresponding sampling points, performs feature weighting on the fuzzy subsets, and obtains the optimal fuzzy subset for the target sampling points, thereby reducing control errors.

[0061] After dynamic adjustment of the fuzzy subset, the adjusted fuzzy PI controller is combined with droop control to suppress voltage fluctuations. For voltage fluctuations occurring in mobile module energy storage, fluctuation suppression is achieved through a dual closed-loop system combining the fuzzy PI controller and droop control. The detailed operation steps are as follows:

[0062] First, the instantaneous voltage and current signals collected are sent to the calculation module, and the active power and reactive power output by the inverter are calculated using instantaneous power theory. The calculated active power and reactive power are then sent to the droop control loop.

[0063] Secondly, the system adjusts the frequency reference value appropriately according to the droop factor based on the current output active power; simultaneously, it adjusts the voltage amplitude reference value based on the current output reactive power. The voltage reference command generated by the droop control is compared with the measured voltage and then sent to the voltage outer loop controller.

[0064] Next, the output of the voltage outer loop controller (a fuzzy PI controller) serves as the reference command for the current inner loop. This current reference command is then compared with the measured current and sent to the current inner loop controller. The current inner loop (PI controller) generates the final control signal. The modulated wave control signal generated by the current inner loop is sent to the pulse width modulation (PWM) unit, compared with the carrier wave, and generates a series of high-frequency switching pulses to drive the power switching devices (such as IGBTs) of the inverter bridge arm to turn on and off, resulting in the high-frequency pulse voltage waveform output by the inverter bridge arm.

[0065] Finally, the voltage is filtered by an LC filter composed of inductors and capacitors to smooth out high-frequency switching harmonics and generate a high-quality, compliant sinusoidal voltage to supply the load, thus completing the voltage fluctuation suppression of the mobile module energy storage.

[0066] Based on the same inventive concept as the above methods, this application also provides a voltage fluctuation suppression system for mobile modular energy storage, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the methods described above.

[0067] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0068] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some technical features, without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions in the embodiments of this application, should all be included within the protection scope of this application.

Claims

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5. The method of claim 1, wherein the method is a mobile modular energy storage voltage fluctuation suppression method, characterized by, The first influence feature and the second influence feature are obtained, and specifically: For each temperature sampling moment, the DTW distance between the sequence composed of all load current features and the sequence composed of all voltage features is calculated, denoted as the first feature index of the voltage feature at each temperature sampling moment; and based on the first feature index and the first discrete degree, the first influence feature of the voltage at each temperature sampling moment is obtained. The DTW distance between the sequence composed of all temperature features and the sequence composed of all voltage features is calculated, denoted as the second feature index of the voltage feature at each temperature sampling moment; and based on the second feature index and the second discrete degree, the second influence feature of the voltage at each temperature sampling moment is obtained.

6. A mobile modular energy storage voltage fluctuation suppression method as claimed in claim 5, characterized in that, The first influence feature is specifically the ratio of the first feature index to the first discrete degree; and the second influence feature is specifically the ratio of the second feature index to the second discrete degree.

7. The method of claim 1, wherein the method is a mobile modular energy storage voltage fluctuation suppression method. The domain of the fuzzy PI control is adjusted, and specifically: the sum of the natural number 1 and the dynamic influence index corresponding to each temperature collection moment is calculated, and then multiplied by a preset domain to obtain the dynamically adjusted domain of each temperature collection moment. 8.A mobile module energy storage voltage fluctuation suppression system, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1-7.

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