Dynamic voltage restorer control method based on super capacitor

Abstract

The invention discloses a dynamic voltage restorer control method based on a supercapacitor, and relates to the related field of power systems, and the method comprises the steps: collecting the current information and voltage information of a to-be-monitored circuit through a current sensor and a voltage sensor; an equivalent circuit topology of a to-be-monitored circuit is constructed, and a circuit control mode is configured; performing backtracking analysis based on the current information and the circuit control mode to obtain a target voltage; when the voltage information does not belong to the target voltage, environment monitoring characteristics are collected through an environment sensor; according to the equivalent circuit topology, the environment monitoring characteristics and the target voltage, performing discharge optimization on the supercapacitor to generate a recommended discharge rate; and performing recovery control on the voltage information according to the recommended discharge rate. The technical problem of insufficient control precision caused by lack of quantitative analysis on environmental influence in the control of the existing dynamic voltage restorer is solved, and the technical effect of improving the control precision of the dynamic voltage restorer is achieved.

Description

Technical Field

[0001] The present application relates to the field of power systems, and in particular to a method for controlling a dynamic voltage restorer based on a supercapacitor. Background Art

[0002] In the power system, voltage stability is one of the key factors to ensure power quality and power supply reliability. With the increasing complexity and diversification of power loads, especially in modern power grids containing a large number of nonlinear loads and sensitive electronic devices, power quality problems such as voltage sag and voltage fluctuation are becoming more and more prominent. As an effective power electronic device, dynamic voltage restorer (DVR) is widely used to solve such problems, ensuring the stable operation of sensitive loads by quickly compensating for voltage fluctuations. Among them, supercapacitors have become a commonly used energy storage element in DVR due to their high power density, long cycle life and fast charging and discharging capabilities. However, in the actual application of DVR, most existing methods use fixed control modes and parameters, lack the ability to adapt to different working conditions and environmental conditions, resulting in a decrease in the compensation accuracy and response speed of DVR when the environment changes, making it difficult to meet the requirements of high precision and high reliability.

[0003] In the current related technologies, the dynamic voltage restorer control lacks quantitative analysis of the impact on the environment, resulting in a technical problem of insufficient control accuracy. Summary of the invention

[0004] The present application provides a dynamic voltage restorer control method based on a supercapacitor, which collects current information and voltage information of a circuit to be monitored, constructs an equivalent circuit topology, configures a circuit control mode, performs a backtracking analysis based on the current information and the circuit control mode, predicts and determines a target voltage, and when the voltage information deviates from the target voltage, collects environmental monitoring characteristics, combines the equivalent circuit topology and the target voltage, performs discharge optimization on the supercapacitor, generates a recommended discharge rate, and adjusts the discharge process of the supercapacitor according to the recommended discharge rate, thereby achieving rapid and accurate recovery of voltage information and achieving the technical effect of improving the control accuracy of the dynamic voltage restorer.

[0005] The present application provides a supercapacitor-based dynamic voltage restorer control method, comprising:

[0006] The current information and voltage information of the circuit to be monitored are collected through current sensors and voltage sensors; an equivalent circuit topology of the circuit to be monitored is constructed, and a circuit control mode is configured; a backtracking analysis is performed based on the current information and the circuit control mode to obtain a target voltage; when the voltage information does not belong to the target voltage, environmental monitoring characteristics are collected through environmental sensors; based on the equivalent circuit topology, the environmental monitoring characteristics and the target voltage, the supercapacitor is discharged optimally to generate a recommended discharge rate; and the voltage information is restored and controlled according to the recommended discharge rate.

[0007] In a possible implementation, a backtracking analysis is performed based on the current information and the circuit control mode to obtain a target voltage, and the following processing is performed:

[0008] Based on the current information and the circuit control mode, a historical positive sample transaction set is collected, wherein the historical positive sample transaction set includes a positive sample voltage record data set; bidirectional neighbor segmentation is performed on the positive sample voltage record data set to obtain a non-outlier positive sample voltage record data set; the maximum voltage record data and the minimum voltage record data of the non-outlier positive sample voltage record data set are extracted; and a voltage distribution interval is constructed according to the maximum voltage record data and the minimum voltage record data, and is set as the target voltage.

[0009] In a possible implementation, bidirectional nearest neighbor segmentation is performed on the positive sample voltage record data set to obtain a non-outlier positive sample voltage record data set, and the following processing is performed:

[0010] The first positive sample voltage record data of the positive sample voltage record data set is obtained; from the positive sample voltage record data set, a preset number of voltage record data are selected from near to far based on the first positive sample voltage record data to construct a first positive neighbor domain; from the positive sample voltage record data set, positive sample voltage record data whose positive neighbor domain includes the first positive sample voltage record data are screened to construct a first reverse neighbor domain; and the positive sample voltage record data set is subjected to bidirectional neighbor segmentation according to the first forward neighbor domain and the first reverse neighbor domain to obtain the non-outlier positive sample voltage record data set.

[0011] In a possible implementation, bidirectional nearest neighbor segmentation is performed on the positive sample voltage record data set according to the first forward nearest neighbor and the first reverse nearest neighbor to obtain the non-outlier positive sample voltage record data set, and the following processing is performed:

[0012] Calculate the first distance mean between the positive sample voltage record data of the first positive neighboring domain and the first reverse neighboring domain and the first positive sample voltage record data, and add them into the distance mean set; calculate the mean of the distance mean set, and set it as the global distance mean; calculate the ratio of the first distance mean to the global distance mean, and obtain a first segmentation coefficient; when the first segmentation coefficient is greater than or equal to the segmentation coefficient threshold, segment the first positive sample voltage record data from the positive sample voltage record data set; when the traversal of the positive sample voltage record data set is completed, obtain the non-outlier positive sample voltage record data set.

[0013] In a possible implementation, the supercapacitor is discharged optimally according to the equivalent circuit topology, the environmental monitoring characteristics and the target voltage, a recommended discharge rate is generated, and the following processing is performed:

[0014] Perform Ohm's law analysis according to the target voltage and the equivalent circuit topology to obtain the target current; obtain the compensation current based on the target current and the current information and the equivalent circuit topology; and perform a discharge rate analysis on the supercapacitor according to the environmental monitoring characteristics and the compensation current to obtain the recommended discharge rate.

[0015] In a possible implementation, the discharge rate of the supercapacitor is analyzed according to the environmental monitoring characteristics and the compensation current to obtain the recommended discharge rate, and the following processing is performed:

[0016] The theoretical discharge rate is calculated according to the compensation current and the response time of the supercapacitor; the discharge rate influence coefficient prediction model is activated according to the supercapacitor model; the environmental monitoring characteristics are analyzed according to the discharge rate influence coefficient prediction model to obtain the discharge rate influence coefficient; the recommended discharge rate is obtained by dividing the theoretical discharge rate by the discharge rate influence coefficient.

[0017] In a possible implementation, the discharge rate influence coefficient prediction model is activated through the supercapacitor model, and the following processing is performed:

[0018] Based on the supercapacitor model, with the environmental indicator as the only variable, the environmental indicator recorded data, the target discharge rate recorded data and the actual discharge rate recorded data are collected; the ratio of the actual discharge rate recorded data to the target discharge rate recorded data is calculated and set as the discharge rate influence coefficient identification data; with the discharge rate influence coefficient identification data as supervision and the environmental indicator recorded data as input, the discharge rate influence coefficient prediction model is configured.

[0019] In a possible implementation, when the voltage information does not belong to the target voltage, the following processing is performed:

[0020] Obtain voltage information at past continuous moments and theoretical voltage information at past continuous moments; calculate the proportion of the voltage information at past continuous moments that does not belong to the theoretical voltage information at past continuous moments, and set it as a first abnormal proportion; when the first abnormal proportion is greater than or equal to an abnormal proportion threshold, the voltage information does not belong to the target voltage.

[0021] The dynamic voltage restorer control method based on supercapacitor proposed in this application first collects current information and voltage information of the circuit to be monitored through current sensors and voltage sensors, then constructs an equivalent circuit topology of the circuit to be monitored, configures the circuit control mode, and then performs backtracking analysis based on the current information and circuit control mode to obtain the target voltage. When the voltage information does not belong to the target voltage, the environmental monitoring characteristics are collected through the environmental sensor, and then the supercapacitor is discharged optimally according to the equivalent circuit topology, environmental monitoring characteristics and target voltage, and a recommended discharge rate is generated. Finally, the voltage information is restored and controlled according to the recommended discharge rate, thereby achieving the technical effect of improving the control accuracy of the dynamic voltage restorer. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the method according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic flow chart of a supercapacitor-based dynamic voltage restorer control method provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of a process for obtaining a non-outlier positive sample voltage recording data set in a supercapacitor-based dynamic voltage restorer control method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0026] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0027] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0028] The present application embodiment provides a method for controlling a dynamic voltage restorer based on a supercapacitor, such as Figure 1 As shown, the method includes:

[0029] Step S100, collect the current information and voltage information of the circuit to be monitored through the current sensor and the voltage sensor. Specifically, based on the rated voltage and current range of the circuit, high-precision current sensors and voltage sensors are installed at appropriate positions of the circuit to be monitored, wherein the current sensor is used to measure the current in the circuit to be monitored to reflect the current changes in the circuit to be monitored in real time; the voltage sensor is used to measure the voltage in the circuit to be monitored. After the installation is completed, a comparison test is performed using a known current and voltage source, and the current sensor and the voltage sensor are calibrated to ensure the accuracy of their measurement results. Start the sensor, set the sampling rate and resolution of the sensor, and collect the current and voltage signals in the circuit to be monitored in real time. The collected original current and voltage signals are preprocessed, including filtering, denoising and other operations to eliminate interference and noise in the signal and improve the reliability of the data.

[0030] Step S200, construct the equivalent circuit topology of the circuit to be monitored and configure the circuit control mode. Specifically, the circuit to be monitored is analyzed in detail to determine its structure, constituent elements (such as resistance, inductance, capacitance, load, etc.) and the connection mode between the elements, and the main functions and operating characteristics of the circuit to be monitored, including the input-output relationship, steady-state and transient characteristics of the circuit. According to the circuit analysis results, the equivalent model of the circuit to be monitored, that is, the equivalent circuit topology, is constructed using circuit theory and methods (such as Kirchhoff's law, node voltage method, loop current method, etc.). The equivalent circuit topology is used to reflect the main characteristics and behaviors of the circuit to be monitored, and at the same time simplifies the circuit structure for easy analysis and control. According to the operating requirements and goals of the circuit to be monitored (such as voltage stability, power factor correction, harmonic suppression, etc.), the control strategy and mode are configured, wherein the control mode refers to the control strategy and parameter configuration of the circuit under different working conditions, including open-loop control, closed-loop control, adaptive control, fuzzy control, neural network control and other types.

[0031] Step S300, based on the current information and the circuit control mode, a backtracking analysis is performed to obtain a target voltage. Specifically, according to the configured circuit control mode, a control strategy and parameters suitable for the current state of the circuit to be monitored are determined, including the gain, phase, response time, etc. of the controller. Backtracking analysis is performed using the current information and the circuit control mode. The backtracking analysis is a reverse reasoning process. Starting from the current circuit state, the circuit state or parameters that meet specific conditions (such as voltage stability, power balance, etc.) are reversely calculated, that is, the target voltage that can maintain stable operation of the circuit or meet specific performance requirements under given current information and circuit control mode is found. The backtracking analysis can be performed through mathematical models, algorithms or simulation software to simulate the behavior of the circuit to be monitored under different current conditions and predict the voltage response under different conditions. According to the results of the backtracking analysis and the control target, the target voltage is determined. The target voltage is the voltage level that the dynamic voltage restorer strives to maintain or restore, reflecting the requirements of the circuit to be monitored for voltage stability and quality.

[0032] In a possible implementation, a backtracking analysis is performed based on the current information and the circuit control mode to obtain the target voltage, and step S300 further includes step S310, based on the current information and the circuit control mode, a historical positive sample transaction set is collected, wherein the historical positive sample transaction set includes a positive sample voltage record data set. Specifically, historical records matching the current current information and circuit control mode are retrieved from the database, and historical transactions that successfully maintain or recover to a stable voltage state are screened out to form a historical positive sample transaction set, wherein the historical positive sample transaction set includes multiple positive sample voltage record data sets, each positive sample voltage record data set corresponds to a successful voltage recovery or maintenance event, and includes voltage, current, timestamp and other information. Step S320, bidirectional nearest neighbor segmentation is performed on the positive sample voltage record data set to obtain a non-outlier positive sample voltage record data set. Specifically, a bidirectional nearest neighbor analysis is performed on each positive sample voltage recording data set, that is, the data points before and after each voltage recording data point are analyzed, and the voltage recording data points that are significantly different from the surrounding data points (i.e., outliers) are identified and eliminated. These voltage recording data points are caused by noise, abnormal operation or equipment failure, and the remaining voltage recording data points that are consistent with the surrounding data points are retained to form a non-outlier positive sample voltage recording data set. Step S330, extract the maximum voltage recording data and the minimum voltage recording data of the non-outlier positive sample voltage recording data set; step S340, construct a voltage distribution interval based on the maximum voltage recording data and the minimum voltage recording data, and set it as the target voltage. Specifically, the non-outlier positive sample voltage recording data set is traversed to find and record the maximum voltage value and the minimum voltage value therein, which respectively represent the upper limit and lower limit of the non-outlier positive sample voltage recording data set in the voltage dimension. Use the maximum voltage recording data and the minimum voltage recording data to define a voltage interval, i.e., the target voltage range, which represents the expected voltage stability range under the current information and circuit control mode. This implementation method achieves the technical effect of improving the accuracy of the target voltage by collecting a set of historical positive sample transactions and performing bidirectional neighbor segmentation, eliminating noise and abnormal data, and retaining the data points that best represent the expected voltage behavior.

[0033] In a possible implementation, the positive sample voltage record data set is subjected to bidirectional nearest neighbor segmentation to obtain a non-outlier positive sample voltage record data set, and step S320 further includes step S321, obtaining the first positive sample voltage record data of the positive sample voltage record data set. Specifically, a positive sample voltage record data is randomly selected from the positive sample voltage record data set as the first positive sample voltage record data, as the starting point for constructing a nearest neighbor. Step S322, from the positive sample voltage record data set, a preset number of voltage record data are selected from the first positive sample voltage record data from near to far, to construct a first positive nearest neighbor. Specifically, for the first positive sample voltage record data, the distance (such as Euclidean distance, Manhattan distance, etc.) between it and all other voltage record data in the positive sample voltage record data set is calculated, and the voltage record data is sorted according to the calculated distance, and a preset number (such as k) of voltage record data closest to the first positive sample voltage record data is selected, and these selected voltage record data are combined to form a first positive nearest neighbor of the first positive sample voltage record data. Step S323, from the positive sample voltage record data set, filter the positive sample voltage record data whose forward neighbor includes the first positive sample voltage record data, and construct the first reverse neighbor. Specifically, traverse the entire positive sample voltage record data set, find the voltage record data whose forward neighbor contains the first positive sample voltage record data, and combine these voltage record data to form the first reverse neighbor. Step S324, perform bidirectional neighbor segmentation on the positive sample voltage record data set according to the first forward neighbor and the first reverse neighbor, and obtain the non-outlier positive sample voltage record data set. Specifically, combine the information of the first forward neighbor and the first reverse neighbor, evaluate each voltage record data in the positive sample voltage record data set, identify those voltage record data that are neither in the forward neighbor of other points nor in the reverse neighbor of enough points, these data are regarded as outliers, and are removed from the positive sample voltage record data set. After the above processing, the remaining data set is the non-outlier positive sample voltage record data set. This implementation method comprehensively evaluates the relationship between data points through bidirectional nearest neighbor segmentation, achieving the technical effect of enhancing the robustness of the entire processing process.

[0034] like Figure 2As shown, in a possible implementation, the positive sample voltage record data set is bidirectionally segmented according to the first positive neighbor domain and the first reverse neighbor domain to obtain the non-outlier positive sample voltage record data set, and step S324 further includes step S3241, calculating the first distance mean of the positive sample voltage record data of the first positive neighbor domain and the first reverse neighbor domain and the first positive sample voltage record data, and adding it to the distance mean set. Specifically, create an empty distance mean set to store the distance mean of each positive sample voltage record data and other data in its neighbor domain (forward and reverse). For each positive sample voltage record data (set as the first positive sample voltage record data), calculate the distance (such as Euclidean distance) of all data in its forward neighbor domain and reverse neighbor domain respectively, sum the distances of the forward neighbor domain and the reverse neighbor domain respectively, and then divide them by the number of data points in each neighbor domain to obtain two distance means, and then calculate the mean of the two distance means, and add the calculated mean to the distance mean set. Step S3242, calculate the mean of the distance mean set and set it as the global distance mean. Specifically, add up all the values ​​in the distance mean set, divide the accumulated sum by the number of elements in the distance mean set, and obtain the global distance mean. Step S3243, calculate the ratio of the first distance mean to the global distance mean, and obtain the first segmentation coefficient. Specifically, find the distance mean (first distance mean) corresponding to the currently traversed positive sample voltage record data from the distance mean set, divide the first distance mean by the global distance mean, and obtain the first segmentation coefficient. The first segmentation coefficient represents a quantitative indicator of the similarity between the current positive sample voltage record data and its neighboring data relative to the overall data set, which is used to determine whether the data is regarded as an outlier. Step S3244, when the first segmentation coefficient is greater than or equal to the segmentation coefficient threshold, segment the first positive sample voltage record data from the positive sample voltage record data set. Specifically, the first segmentation coefficient is compared with a preset segmentation coefficient threshold value. The segmentation coefficient threshold value is a threshold value set according to actual needs for determining whether a data point is an outlier. If the first segmentation coefficient is greater than or equal to the segmentation coefficient threshold value, it is considered that the data point has a low similarity with the overall data set and is an outlier, so it is removed from the positive sample voltage recording data set. Step S3245, when the traversal of the positive sample voltage recording data set is completed, the non-outlier positive sample voltage recording data set is obtained. Specifically, when all positive sample voltage recording data are processed through the above steps, the traversal is completed, and the data remaining in the positive sample voltage recording data set is the non-outlier positive sample voltage recording data set. This implementation method quantitatively evaluates the similarity or abnormality of each data point by calculating the mean distance between each data point and its immediate neighborhood, and comparing the difference between these means and the global mean, thereby achieving the technical effect of accurately identifying and removing outliers in the positive sample voltage recording data set.

[0035] Step S400, when the voltage information does not belong to the target voltage, collect environmental monitoring characteristics through environmental sensors. Specifically, the real-time voltage information obtained from the voltage sensor is compared with the determined target voltage. If the real-time voltage information is consistent with the target voltage within a preset tolerance range, the voltage of the circuit to be monitored is considered normal and no further environmental characteristic collection is required; when the real-time voltage information does not meet the target voltage requirements, the environmental sensor is triggered to start working. The environmental sensor includes a temperature sensor, a humidity sensor, a light sensor, a wind speed sensor, etc. Various characteristic parameters in the current environment, such as temperature, humidity, light intensity, wind speed, etc., are collected through the environmental sensor.

[0036] In a possible implementation, when the voltage information does not belong to the target voltage, step S400 further includes step S410, obtaining the voltage information of the past continuous moments and the theoretical voltage information of the past continuous moments. Specifically, the past continuous moments refer to multiple time points within a time window, which are continuous and fixed at intervals (such as every second, every minute), and the voltage information of the past continuous moments refers to the actual voltage value of each time point in the past period of time (such as the last few minutes, hours). The theoretical voltage information of the past continuous moments refers to the theoretical voltage value of the corresponding time point in the past obtained by retrospective analysis based on the current information and the circuit control mode. Step S420, calculate the proportion of the voltage information of the past continuous moments that does not belong to the theoretical voltage information of the past continuous moments, and set it as the first abnormal proportion. Specifically, the actual voltage value of each time point is compared with the theoretical voltage value of the corresponding time point to determine whether they are consistent or within the allowable error range. For the data points that are inconsistent or exceed the error range, count them, divide the abnormal count value by the total number of time points, and obtain the first abnormal proportion. Step S430, when the first abnormal proportion is greater than or equal to the abnormal proportion threshold, the voltage information does not belong to the target voltage. Specifically, an abnormality ratio threshold is set to judge the severity of the voltage abnormality, and the first abnormality ratio is compared with the abnormality ratio threshold. If the former is greater than or equal to the latter, it is judged that the current voltage information does not belong to the target voltage, that is, there is a significant voltage abnormality. This implementation method evaluates the voltage state more comprehensively by considering the voltage data of continuous moments in the past, rather than just the voltage value at the current moment, reduces misjudgments caused by instantaneous fluctuations or noise, and achieves the technical effect of improving the accuracy of judgment and enhancing the robustness and stability of the system.

[0037] Step S500, according to the equivalent circuit topology, the environmental monitoring characteristics and the target voltage, the supercapacitor is discharged and optimized to generate a recommended discharge rate. Specifically, the constructed equivalent circuit topology, the collected environmental monitoring characteristics and the target voltage are integrated to form complete control input data. According to the equivalent circuit topology and the characteristics of the supercapacitor, a mathematical model describing the dynamic behavior of the circuit is established, which can reflect the change law of the circuit voltage during the discharge of the supercapacitor. The optimization goal is clarified, that is, minimizing the voltage deviation or maximizing the voltage recovery speed, while combining the energy utilization efficiency and service life of the supercapacitor. Using optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, gradient descent methods, etc., under given environmental monitoring characteristics and target voltages, the discharge rate of the supercapacitor is optimized to find the optimal discharge rate that meets the optimization goal, wherein the discharge rate refers to the amount of charge released by the supercapacitor per unit time. According to the optimization results, a recommended discharge rate is generated, which is the discharge rate that the supercapacitor should adopt under the current environment and target voltage to achieve rapid recovery control of the voltage.

[0038] In a possible implementation, the supercapacitor is discharged optimally according to the equivalent circuit topology, the environmental monitoring characteristics and the target voltage to generate a recommended discharge rate, and step S500 further includes step S510, performing Ohm's law analysis according to the target voltage and the equivalent circuit topology to obtain the target current. Specifically, based on the target voltage and the parameters of all relevant components in the equivalent circuit topology (the connection mode of the supercapacitor in the circuit to be monitored, the distribution and parameters of components such as resistance and inductance), Ohm's law is used to calculate the target current value that needs to be achieved in theory during the discharge process. Step S520, based on the target current and the current information, based on the equivalent circuit topology, a compensation current is obtained. Specifically, the target current is compared with the actual current to find the difference between the two. Based on the equivalent circuit topology, the cause of the current difference (such as resistance change, temperature effect, inductance effect, etc.) is analyzed, and the compensation current value that needs to be adjusted is calculated to reduce or eliminate the difference between the target current and the actual current, so that the actual current is closer to the target current. Step S530, according to the environmental monitoring characteristics and the compensation current, the discharge rate of the supercapacitor is analyzed to obtain the recommended discharge rate. Specifically, the influence of environmental factors such as ambient temperature, humidity, and air pressure on the discharge performance of the supercapacitor is analyzed. These factors affect the discharge rate by affecting the internal resistance, capacity and other parameters of the supercapacitor. The comprehensive influence on the discharge rate is evaluated by combining the compensation current and the environmental monitoring characteristics. Based on the above analysis, a mathematical model or algorithm (such as an empirical formula, a simulation model, etc.) is used to calculate the recommended discharge rate that can meet the target voltage requirements and adapt to the current environmental conditions and circuit characteristics, and the calculation result is output as the recommended discharge rate for use by the supercapacitor control system. This implementation method can more accurately control the discharge process of the supercapacitor through Ohm's law analysis and compensation current calculation, ensure that the discharge rate matches the target voltage and circuit characteristics, and achieve the technical effect of improving the accuracy of discharge optimization.

[0039] In a possible implementation, the discharge rate of the supercapacitor is analyzed according to the environmental monitoring characteristics and the compensation current to obtain the recommended discharge rate. Step S530 further includes step S531, calculating the theoretical discharge rate according to the compensation current and the response time of the supercapacitor. Specifically, the compensation current represents the current that the supercapacitor needs to release under specific environmental conditions. The time period from when the system issues a discharge command to when the supercapacitor begins to discharge stably, that is, the response time, is recorded. The response time includes circuit delays, controller response time, etc. The theoretical discharge rate is calculated using the compensation current and the initial energy (or capacity) of the supercapacitor and the response time. For example, the compensation current is divided by the proportion of energy that the capacitor can release within the response time. Step S532, activates the discharge rate influence coefficient prediction model through the supercapacitor model. Specifically, the model of the currently connected supercapacitor is identified. Supercapacitors of different models have different discharge characteristics. According to the model of the supercapacitor, the corresponding discharge rate influence coefficient prediction model is loaded from the pre-stored database or cloud service. The discharge rate influence coefficient prediction model can be a machine learning model trained based on a large amount of experimental data, which is used to predict the discharge rate influence coefficient, wherein the discharge rate influence coefficient is a coefficient that reflects the influence of environmental factors (such as temperature, humidity, etc.) on the discharge rate of the supercapacitor. Step S533, the environmental monitoring characteristics are analyzed by the discharge rate influence coefficient prediction model to obtain the discharge rate influence coefficient. Specifically, the collected environmental monitoring data is used as input and passed to the discharge rate influence coefficient prediction model. The discharge rate influence coefficient prediction model outputs the corresponding discharge rate influence coefficient according to the input environmental monitoring data. Step S534, the theoretical discharge rate is divided by the discharge rate influence coefficient to obtain the recommended discharge rate. This implementation method takes into account the complexity and variability of supercapacitors in actual working environments. By adjusting the theoretical discharge rate in combination with environmental monitoring characteristics, it is closer to the actual application scenario, and achieves the technical effect of improving the accuracy and reliability of the recommended discharge rate.

[0040] In a possible implementation, the discharge rate influence coefficient prediction model is activated by the supercapacitor model, and step S532 further includes step S5321, based on the supercapacitor model, with the environmental indicator as the only variable, collecting environmental indicator recording data, target discharge rate recording data and actual discharge rate recording data. Specifically, according to the model, characteristics and application scenarios of the supercapacitor, a series of environmental indicators that may affect its discharge rate, such as temperature, humidity, air pressure, etc., are determined. Under multiple different environmental conditions (i.e., different combinations of environmental indicators), the environmental indicator recording data, target discharge rate recording data and actual discharge rate recording data of the supercapacitor are recorded. Among them, the target discharge rate is the discharge rate expected or set by the system, and the actual discharge rate is the discharge rate actually measured under given environmental conditions. Step S5322, calculate the ratio of the actual discharge rate recording data to the target discharge rate recording data, and set it as the discharge rate influence coefficient identification data. Specifically, the target discharge rate record data and the actual discharge rate record data corresponding to each environmental indicator record data are made consistent in time. For each environmental indicator record data, the ratio of the corresponding actual discharge rate record data to the target discharge rate record data is calculated, that is, the discharge rate influence coefficient identification data. The discharge rate influence coefficient identification data reflects the degree of deviation of the actual discharge rate from the target discharge rate under the current environmental conditions, that is, the degree of influence of the environment on the discharge rate. Step S5323, using the discharge rate influence coefficient identification data as supervision and the environmental indicator record data as input, configure the discharge rate influence coefficient prediction model. Specifically, according to the characteristics of the data and the prediction requirements, select the prediction model type, such as linear regression, neural network, decision tree, etc. Take the environmental indicator record data as input and the discharge rate influence coefficient identification data as supervision (or target), and train the selected prediction model. During the training process, the model will learn the relationship between the environmental indicator and the discharge rate influence coefficient. After the training is completed, the discharge rate influence coefficient prediction model is obtained, and the discharge rate influence coefficient prediction model can predict the discharge rate influence coefficient according to the input environmental indicator. This implementation method establishes an accurate prediction model by collecting a large amount of environmental indicator record data and corresponding discharge rate data, achieving the technical effect of accurately predicting the discharge rate influence coefficient under different environmental conditions.

[0041] Step S600, the voltage information is restored and controlled according to the recommended discharge rate. Specifically, according to the generated recommended discharge rate, a control signal for controlling the discharge of the supercapacitor is calculated and generated. This control signal can be a voltage instruction, a current instruction or a power instruction, depending on the implementation method of the supercapacitor discharge control circuit. The control signal is sent to the supercapacitor discharge control circuit to control the supercapacitor to discharge according to the recommended discharge rate. During the discharge process, the voltage information of the circuit to be monitored is continuously monitored by the voltage sensor, and the voltage information monitored in real time is compared with the target voltage to evaluate the effect of voltage recovery. If the voltage recovery effect is found to be unsatisfactory during the discharge process (such as excessive voltage fluctuations, too slow recovery speed, etc.), the recommended discharge rate can be dynamically adjusted based on the real-time monitored voltage information and environmental monitoring characteristics, based on feedback control algorithms such as PID control, fuzzy control, etc. When the voltage information is restored to the target voltage range and remains stable for a period of time, it is considered that the voltage recovery control is successfully completed. At this time, the discharge of the supercapacitor can be stopped and the relevant control circuit can be turned off. The embodiment of the present application adopts technical means such as collecting current information and voltage information of the circuit to be monitored, constructing an equivalent circuit topology, configuring the circuit control mode, performing backtracking analysis based on the current information and the circuit control mode, predicting and determining the target voltage, and when the voltage information deviates from the target voltage, collecting environmental monitoring characteristics, combining the equivalent circuit topology and the target voltage, optimizing the discharge of the supercapacitor, generating a recommended discharge rate, and adjusting the discharge process of the supercapacitor according to the recommended discharge rate, thereby achieving rapid and accurate recovery of the voltage information and achieving the technical effect of improving the control accuracy of the dynamic voltage restorer.

[0042] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A dynamic voltage restorer control method based on supercapacitor, characterized in that: include: Collect current information and voltage information of the circuit to be monitored through current sensors and voltage sensors; Constructing an equivalent circuit topology of the circuit to be monitored and configuring a circuit control mode; Performing a backtracking analysis based on the current information and the circuit control mode to obtain a target voltage; When the voltage information does not belong to the target voltage, collecting environmental monitoring characteristics through an environmental sensor; Optimizing the discharge of the supercapacitor according to the equivalent circuit topology, the environmental monitoring characteristics and the target voltage, and generating a recommended discharge rate; The voltage information is restored and controlled according to the recommended discharge rate.

2. The supercapacitor-based dynamic voltage restorer control method according to claim 1, characterized in that: Performing a backtracking analysis based on the current information and the circuit control mode to obtain a target voltage includes: Based on the current information and the circuit control mode, a historical positive sample transaction set is collected, wherein the historical positive sample transaction set includes a positive sample voltage record data set; Performing bidirectional nearest neighbor segmentation on the positive sample voltage record data set to obtain a non-outlier positive sample voltage record data set; Extracting maximum voltage record data and minimum voltage record data of the non-outlier positive sample voltage record data set; A voltage distribution interval is constructed based on the maximum voltage recorded data and the minimum voltage recorded data, and is set as the target voltage.

3. The supercapacitor-based dynamic voltage restorer control method according to claim 2, characterized in that: Performing bidirectional nearest neighbor segmentation on the positive sample voltage record data set to obtain a non-outlier positive sample voltage record data set, including: Obtaining first positive sample voltage record data of the positive sample voltage record data set; From the positive sample voltage record data set, a preset number of voltage record data are selected from near to far based on the first positive sample voltage record data to construct a first positive near neighborhood; From the positive sample voltage record data set, screening the positive sample voltage record data whose positive neighbor domain includes the first positive sample voltage record data, and constructing a first reverse neighbor domain; The positive sample voltage record data set is subjected to bidirectional nearest neighbor segmentation according to the first forward nearest neighbor and the first reverse nearest neighbor to obtain the non-outlier positive sample voltage record data set.

4. The supercapacitor-based dynamic voltage restorer control method according to claim 3, characterized in that: Performing bidirectional nearest neighbor segmentation on the positive sample voltage record data set according to the first positive nearest neighbor domain and the first reverse nearest neighbor domain to obtain the non-outlier positive sample voltage record data set includes: Calculate a first distance mean between the positive sample voltage record data of the first positive neighboring domain and the first reverse neighboring domain and the first positive sample voltage record data, and add the distance mean value to a distance mean set; Calculate the mean of the distance mean set and set it as the global distance mean; Calculate the ratio of the first distance mean to the global distance mean to obtain a first segmentation coefficient; When the first segmentation coefficient is greater than or equal to a segmentation coefficient threshold, segmenting the first positive sample voltage record data from the positive sample voltage record data set; When the traversal of the positive sample voltage record data set is completed, the non-outlier positive sample voltage record data set is obtained.

5. The supercapacitor-based dynamic voltage restorer control method according to claim 1, characterized in that: According to the equivalent circuit topology, the environmental monitoring characteristics and the target voltage, the supercapacitor is discharged optimally to generate a recommended discharge rate, including: Perform Ohm's law analysis according to the target voltage and the equivalent circuit topology to obtain a target current; Obtaining a compensation current according to the target current and the current information and based on the equivalent circuit topology; The discharge rate of the supercapacitor is analyzed according to the environmental monitoring characteristics and the compensation current to obtain the recommended discharge rate.

6. The supercapacitor-based dynamic voltage restorer control method according to claim 5, characterized in that: According to the environmental monitoring characteristics and the compensation current, the discharge rate of the supercapacitor is analyzed to obtain the recommended discharge rate, including: Calculating a theoretical discharge rate according to the compensation current and the response time of the supercapacitor; Activate the discharge rate influence coefficient prediction model through the supercapacitor model; Analyze the environmental monitoring characteristics through the discharge rate influence coefficient prediction model to obtain the discharge rate influence coefficient; The recommended discharge rate is obtained by dividing the theoretical discharge rate by the discharge rate influence coefficient.

7. The supercapacitor-based dynamic voltage restorer control method according to claim 6, characterized in that: Activate the discharge rate influence coefficient prediction model based on the supercapacitor model, including: Based on the supercapacitor model, with the environmental index as the only variable, collecting environmental index record data, target discharge rate record data and actual discharge rate record data; Calculating a ratio of the actual discharge rate record data to the target discharge rate record data, and setting the ratio as discharge rate influence coefficient identification data; The discharge rate influence coefficient identification data is used as supervision and the environmental indicator record data is used as input to configure the discharge rate influence coefficient prediction model.

8. The supercapacitor-based dynamic voltage restorer control method according to claim 1, characterized in that: When the voltage information does not belong to the target voltage, it includes: Obtaining voltage information at continuous moments in the past and theoretical voltage information at continuous moments in the past; Calculate the proportion of the voltage information at the past continuous moments that does not belong to the theoretical voltage information at the past continuous moments, and set it as a first abnormal proportion; When the first abnormality ratio is greater than or equal to an abnormality ratio threshold, the voltage information does not belong to the target voltage.

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