Magnetic reactance type dynamic voltage recovery device and method based on particle swarm optimization

Abstract

The invention provides a magnetic reactance type dynamic voltage recovery device and method based on particle swarm optimization, and relates to the technical field of dynamic voltage recovery devices.The method comprises the steps that depth time sequence analysis is conducted on historical drop voltage, the amplitude and duration of voltage drop are dynamically predicted, and therefore the amplitude interval and duration of injection voltage are determined in a self-adaptive mode; the particle swarm optimization is combined to perform multi-objective optimization on the control scheme, and the performance of the scheme is fed back and evaluated, so that accurate design and real-time adjustment of the injected voltage waveform are realized; a multi-scheme screening and switching mechanism effectively guarantees stable operation and safety of the system under abnormal working conditions, the intelligent level and compensation quality of the dynamic voltage recovery device are integrally improved, the requirements for quick response and efficient compensation in a complex power grid environment are met, and the overall stability and reliability of the system are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dynamic voltage recovery devices, in particular to a magnetic resistance type dynamic voltage recovery device and method based on particle swarm optimization. BACKGROUND

[0002] With the continuous development of power systems, the power grid often encounters voltage drop problems when dealing with load fluctuations, equipment maintenance and transient disturbances, which seriously affects power supply quality and user safety. Traditional dynamic voltage recovery devices usually use fixed parameters or experience-based compensation strategies, which are difficult to adapt to the variability and complexity of voltage drops, especially in different working conditions, making it difficult to achieve accurate compensation control, resulting in unstable compensation effect, increased equipment burden, and even safety risks.

[0003] In the prior art, a magnetic resistance type dynamic voltage recovery device optimization configuration method and device are disclosed in CN120999649A, which relates to the technical field of optimization configuration, specifically including: obtaining historical voltage sag event data of the target distribution bus and electrical parameters of the downstream load motor; filtering the compensation event set based on the principle that the sag depth exceeds the load tolerance threshold and the duration is less than the critical stall time, and determining the compensation strategy according to the comparison result of the maximum phase jump angle and the threshold; determining the maximum compensation voltage according to the maximum sag depth, determining the rated output current according to the load parameters, calculating the theoretical energy required for the longest compensation event based on the compensation strategy, and determining the actual required energy combined with the energy correction coefficient of the device topology; finally, generating an optimized configuration scheme based on the compensation strategy, the maximum compensation voltage, the rated output current and the actual required energy. Although this scheme can accurately map from the actual measured data of the power grid to the key parameters of the device, it focuses on static optimization and the compensation strategy is single, which cannot achieve adaptive control under different drop events, resulting in insufficient compensation accuracy and low applicability in complex power grid environments.

[0004] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application aims to provide a magnetic resistance type dynamic voltage recovery device and method based on particle swarm optimization to solve the problems raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions: The magnetic resistance type dynamic voltage recovery device based on particle swarm optimization specifically includes a control device and a voltage recovery device body, wherein: The control device includes: A data collection module for collecting historical voltage sag and transmitting it to a time series analysis module; A time series analysis module with a time series analysis model for time series analysis of the historical voltage sag to obtain a predicted voltage sag and generate an amplitude interval of the injected voltage based on the predicted voltage sag; An algorithm optimization module including a data detection unit, an algorithm optimization unit, and a scheme screening unit, wherein: The data detection unit is configured to detect temperature changes and electrical parameter changes generated by the voltage recovery device when generating injected voltages with different amplitudes; The algorithm optimization unit is configured to search within the amplitude interval of the injected voltage and generate several groups of control schemes of the injected voltage using a particle swarm algorithm; The scheme screening unit is configured to screen an optimal scheme and at least one backup scheme from the several groups of control schemes and transmit the optimal scheme and the backup scheme to the voltage recovery device; A signal feedback module including a voltage detection unit and a scheme switching unit, wherein: The voltage detection unit is configured to detect the injected voltage generated by the voltage recovery device in real time to obtain a measured value of the injected voltage and transmit it to the scheme switching unit; The scheme switching unit is configured to generate an abnormality index based on the measured value of the injected voltage and control the voltage recovery device to switch the control scheme of the injected voltage when the abnormality index exceeds a preset index threshold; The voltage recovery device is configured to generate injected voltages with different amplitudes by adjusting its magnetic circuit and injected current.

[0007] Preferably, the prediction logic of the amplitude interval of the injected voltage is: The collected historical voltage sag is preprocessed, including but not limited to denoising, outlier removal, normalization, and forming a continuous time series data set; The time series data set formed based on the historical voltage sag is subjected to time series analysis, and the predicted voltage sag and the predicted voltage sag duration are obtained based on the time series analysis results, and the maximum and minimum values of the predicted voltage sag are used as the upper and lower limits of the amplitude interval of the injected voltage.

[0008] Preferably, the working logic of the algorithm optimization module is: The duration of the injected voltage is determined based on the predicted voltage sag duration, and several groups of control points are uniformly set within the duration, each group of control points corresponding to an injected voltage with a different amplitude, and all control points together constitute a control scheme of the injected voltage; The algorithm optimization unit inputs each control scheme generated by the particle swarm optimization algorithm into the voltage recovery device body and detects the temperature change and the electrical parameter change generated thereby using the data detection unit, wherein the electrical parameters include but are not limited to the injected current and the output voltage of the voltage recovery device body; The algorithm optimization unit scores each control scheme according to the detection results output by the data detection unit and transmits the control scheme and the corresponding score value to the scheme screening unit; The scheme screening unit arranges the control schemes in descending order of the score values, takes the control scheme ranked first as the optimal scheme, and takes the control schemes ranked second or after the second as backup schemes.

[0009] Preferably, the algorithm optimization unit scores the control schemes by constructing an objective function; The objective function is obtained based on the detection results output by the data detection unit, and the objective function is composed of a voltage error term, a current impact term, an energy loss term and a temperature rise constraint term respectively multiplied by corresponding normalized weights.

[0010] Preferably, the voltage error term is the reciprocal of the mean square error between the output voltage and a preset target voltage; The current impact term is the reciprocal of the peak value of the injected current; The energy loss term is the reciprocal of the total energy within a continuous time interval, which is calculated based on the injected voltage, the injected current and the time interval between two adjacent control points; The temperature rise constraint term is a conditional function, which is a fixed constant when the temperature change in the detection results does not exceed a preset temperature threshold, and which is negatively correlated with the excess amount of the temperature change when the temperature change in the detection results exceeds the preset temperature threshold.

[0011] Preferably, the abnormality index is the error percentage of the output voltage compared with the target voltage.

[0012] Preferably, the dynamic voltage recovery device further comprises an alarm module, which switches the backup schemes in descending order of the score values one by one when the abnormality index exceeds a preset index threshold until the abnormality index does not exceed the preset index threshold, and issues an alarm if the abnormality index still exceeds the preset index threshold after all the backup schemes are traversed.

[0013] The magnetic resistance type dynamic voltage recovery method based on particle swarm optimization is obtained by executing the dynamic voltage recovery device described above, and the specific steps include: S1: Collecting the historical dropped voltage of the power grid, performing time series analysis to generate a predicted dropped voltage, and generating an amplitude interval of the injected voltage based on the predicted dropped voltage; S2: detecting the temperature change and the electrical parameter change generated by the voltage recovery device body when generating different injection voltages, combining the detection results and the particle swarm algorithm, taking the amplitude interval of the injection voltage as the search range, generating a plurality of sets of control schemes of the injection voltage and scoring; S3: sorting the control schemes based on the score values, and selecting an optimal scheme and at least one standby scheme from the plurality of sets of control schemes; S4: executing the optimal scheme to generate the injection voltage, detecting the injection voltage in real time, generating an abnormal index based on the measured value of the injection voltage, and switching the scheme based on the abnormal index.

[0014] Compared with the prior art, the beneficial effects of the present application are: The present application dynamically predicts the amplitude and duration of voltage sag by deep time series analysis of historical voltage sag, thereby adaptively determining the amplitude interval and duration of the injection voltage; the particle swarm algorithm is combined to multi-objective optimize the control scheme, and the performance of the evaluation scheme is fed back to realize precise design and real-time adjustment of the injection voltage waveform; the multi-scheme screening and switching mechanism effectively guarantees the stable operation and safety of the system under abnormal working conditions, overall improves the intelligent level and compensation quality of the dynamic voltage recovery device, meets the rapid response and efficient compensation demand under complex power grid environment, and enhances the stability and reliability of the overall system. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The figure is a schematic diagram of the module structure of the present application; Figure 2 The figure is a schematic diagram of the overall method flow of the present application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example: Please see Figure 1 The present invention provides a technical solution: The magnetic reactive dynamic voltage recovery device based on particle swarm optimization specifically includes a control device and a voltage recovery device body. The control device further includes: a data acquisition module, a timing analysis module, an algorithm optimization module, and a signal feedback module.

[0019] The data acquisition module can use high-precision voltage sensors (such as voltage transformers VT or voltage sampling circuits) to collect historical voltage drops in the power grid. After analog-to-digital conversion (ADC), the signal is transmitted in real time to the time series analysis module through an industrial-grade communication interface (such as Ethernet, CAN bus, or fiber optic). The collected data can be stored in an embedded memory or cloud database for historical data accumulation and subsequent processing.

[0020] The timing analysis module incorporates a timing analysis model to perform timing analysis on historical voltage drops to obtain predicted voltage drops, and generates the amplitude range of the injected voltage based on the predicted voltage drops. From a hardware perspective, the timing analysis module can be built using an embedded industrial computer or a high-performance DSP / FPGA, while from a software perspective, it can be implemented using an LSTM neural network or an ARIMA model.

[0021] The algorithm optimization module includes a data detection unit, an algorithm optimization unit, and a scheme selection unit. It can also be built using an embedded industrial computer or a high-performance DSP / FPGA, along with corresponding software. Among these: The data detection unit is used to detect the temperature changes and electrical parameter changes generated when the voltage recovery device body generates injection voltages of different amplitudes. The electrical parameters include, but are not limited to, the injection current and output voltage of the voltage recovery device body, and can be composed of temperature sensors, high-precision current sensors, and high-precision voltage sensors.

[0022] The algorithm optimization unit incorporates a particle swarm optimization algorithm to search within the amplitude range of the injected voltage and generate several control schemes for the injected voltage. This allows for the dynamic determination of the upper and lower limits of the injected voltage, ensuring that the amplitude range is neither too wide to avoid invalid searches nor too narrow to guarantee compensation capability.

[0023] The scheme selection unit is used to select the optimal scheme and at least one backup scheme from several control schemes, and transmit the optimal scheme and the backup scheme to the voltage recovery device body.

[0024] The signal feedback module includes a voltage detection unit and a scheme switching unit, wherein: The voltage detection unit is used to detect the injected voltage generated by the voltage recovery device in real time, obtain the measured value of the injected voltage, and transmit it to the scheme switching unit. Similar to the data acquisition module, the voltage detection unit can also use a high-precision voltage sensor for real-time detection.

[0025] The scheme switching unit is used to generate an abnormal index based on the measured value of the injected voltage, and when the abnormal index exceeds the preset index threshold, it controls the voltage recovery device to switch the injection voltage scheme.

[0026] The voltage recovery device mainly consists of a magnetic reactance element (controllable saturable magnetic core reactor), an inverter bridge arm (IGBT or SiC MOSFET device), and a DC energy storage unit (large capacity capacitor or supercapacitor). By saturating and regulating the magnetic permeability of the magnetic core and controlling the inverter current command, it adjusts its own magnetic circuit and injection current to generate injection voltages of different amplitudes. Modular devices can be used.

[0027] The prediction logic for the amplitude range of the injected voltage is as follows: The collected historical voltage drops are preprocessed, including but not limited to noise reduction, outlier removal, and normalization, and a continuous time series dataset is formed. This ensures the quality of the prediction input data, thereby obtaining more accurate voltage drop and duration predictions and reducing misjudgments or overcompensation. A time series analysis is performed on the time series dataset based on historical drop voltages, and the predicted drop voltage and predicted drop duration are obtained based on the time series analysis results. The maximum and minimum values ​​of the predicted drop voltage are used as the upper and lower limits of the amplitude range of the injected voltage, respectively.

[0028] Understandably, voltage dips in the power grid are often triggered by specific events, such as sudden load changes, maintenance, and oscillations. Examples include peak daytime electricity consumption, off-peak nighttime consumption, and differences in electricity consumption between weekdays and holidays, leading to sudden load changes in the grid; routine maintenance activities such as generator start-ups and shutdowns, and line switching; and resonance, oscillations, and transient processes existing in the power grid. These events typically exhibit periodicity; therefore, time-series analysis can be used to capture the periodicity, suddenness, and slow changes in voltage dips, thereby improving the ability to predict injected voltage and enabling adaptive compensation for different voltage dip scenarios.

[0029] The working logic of the algorithm optimization module is as follows: The duration of the injected voltage is determined based on the predicted drop duration. Within this duration, several sets of control points are evenly distributed, each corresponding to an injected voltage of different amplitudes. All control points together constitute a control scheme for the injected voltage. Simply put, the waveform of the required injected voltage is discretized into multiple control points, thereby greatly reducing the control complexity in engineering systems.

[0030] Each time the algorithm optimization unit generates a control scheme using the particle swarm optimization algorithm, it inputs the scheme into the voltage recovery device and uses the data detection unit to detect the resulting temperature and electrical parameter changes.

[0031] The algorithm optimization unit scores each control scheme based on the detection results output by the data detection unit, and transmits the control scheme and its corresponding score value to the scheme selection unit.

[0032] The scheme selection unit ranks the control schemes in descending order of their scores, designating the top-ranked scheme as the optimal scheme and the second-ranked or subsequent schemes as backup schemes.

[0033] In other words, the predicted drop duration is the same as the duration of the injected voltage. Assuming the duration is... Evenly divided into Group of control points, the time interval between adjacent control points is ,have: The injection voltage corresponding to each control point is expressed as follows: subscript Let the index of the control point be used. Then a set of control schemes can be represented in vector form as follows: ; This is equivalent to needing the voltage restoration device body to sequentially... The injected voltage is output, and the duration of each output is... The total output time is .

[0034] For particle swarm optimization, a vector representing a set of control schemes is a particle, with dimension 1. In this embodiment, the number of particles in the particle swarm optimization algorithm is set to 20 to balance search accuracy and computational efficiency, and the maximum number of iterations is set to 50. That is, during random initialization, 20 control schemes are generated, and new control schemes are generated with each iteration. The entire iteration process updates the positions of the 20 particles 50 times, calculating the score values ​​corresponding to these control schemes to obtain the optimal scheme (equivalent to the global optimum) and backup schemes.

[0035] In this step, by discretizing the continuous injection voltage waveform into a finite number of control points, the complex waveform design is transformed into an optimization problem of a finite-dimensional vector. This significantly reduces the continuity and dimensionality of the optimization variables, avoids the computational burden of directly optimizing continuous functions, and improves the convergence speed and stability of the optimization algorithm. Furthermore, the control commands corresponding to the discrete control points at the sampling time points directly match the sampling / execution cycle of the digital controller, facilitating implementation and debugging in the hardware control system. Moreover, each control scheme is verified by a high-precision data detection unit to ensure that the scoring is based on real electrical parameters and temperature characteristics, thereby enhancing the engineering reliability and practical value of the optimization results.

[0036] The algorithm optimization unit scores the control scheme by constructing an objective function; The objective function is obtained based on the detection results output by the data detection unit, and the objective function is composed of voltage error term, current impact term, energy loss term, and temperature rise constraint term multiplied by their respective normalized weights.

[0037] The voltage error term is the reciprocal of the mean square error between the output voltage and the preset target voltage, and its expression is: ; In the formula Indicates the first The voltage error term corresponding to the group control scheme. Indicates the index of the control scheme. Indicates the first The first group control scheme Injection voltage at group control points, This represents the output voltage of the voltage recovery device. It's understandable that the control scheme is used to control the voltage recovery device itself; therefore, the injected voltage at each control point is the voltage the voltage recovery device is intended to achieve, which is the "preset target voltage," and the two are equivalent. However, due to differences in equipment, environmental influences, and other factors, the actual output voltage of the voltage recovery device (i.e., the output terminal voltage) will differ from the target voltage. The smaller the difference, the better the control effect on the voltage recovery device, the smaller the denominator of the voltage error term, and the larger the value of this term.

[0038] The current surge term is the reciprocal of the peak value of the injected current, and its expression is: ; In the formula Indicates the first Current surge term corresponding to the group control scheme Indicates the first In the group control scheme, if the peak value of the injected current is too large, it is considered that the risk of current surge in the voltage recovery device body is greater, and the value of the current surge term will be smaller.

[0039] The energy loss term is the reciprocal of the total energy over the duration. The total energy is calculated from the injection voltage, injection current, and time interval between two adjacent control points, and its expression is: ; In the formula Indicates the first Energy loss items corresponding to the group control scheme. Indicates the voltage recovery device body in the first The first group control scheme The injected current at each control point. As can be seen from the denominator of the energy loss term, it represents the sum of the theoretical power consumption of the voltage recovery device at each control point, which is the total energy consumed over the duration.

[0040] The temperature rise constraint term is a conditional function. When the temperature change in the detection result does not exceed the preset temperature threshold, the temperature rise constraint term is a fixed constant. When the temperature change in the detection result exceeds the preset temperature threshold, the magnitude of the temperature rise constraint term is negatively correlated with the amount of temperature change exceeding the threshold. Its expression is as follows: ; In the formula Indicates the first Temperature rise constraints corresponding to the group control scheme Indicates the amount of temperature change. Indicates the temperature threshold. This represents a fixed constant, the value of which is determined by expert experience. As can be seen from the expression of the temperature rise constraint term, it is actually a penalty term. When the temperature change does not exceed the temperature threshold, the voltage recovery device is considered to be operating normally, the value of the temperature rise constraint term remains unchanged, and it will not affect the control scheme score. However, when the temperature change exceeds the temperature threshold, the greater the exceedance, the smaller the value of the temperature rise constraint term becomes, resulting in a lower score for the control scheme. Furthermore, the use of an exponential function is to improve the sensitivity to temperature and avoid safety risks caused by excessively high operating temperatures of the voltage recovery device.

[0041] The final objective function expression can be represented as: ; in Indicates the first The score value corresponding to the group control scheme ~ Represents the normalized weights, i.e. The weighting of each component is adjusted according to design requirements. For example, if the design requirement is to prioritize accuracy, followed by risk control, and lastly power consumption, then the weights for each component can be set to 0.5, 0.2, 0.1, and 0.2, respectively.

[0042] It is understandable that the voltage recovery device and the load terminal can be controlled by setting a switch. That is to say, when the optimal or backup plan is not executed, the voltage recovery device is not connected to the load terminal, and the injected voltage is only used for data acquisition to generate a control plan. After the optimal or backup plan is determined, the switch can be opened to connect to the load terminal when a voltage drop occurs in the power grid, thereby realizing dynamic voltage compensation.

[0043] The dynamic voltage recovery device also includes an alarm module. The abnormality indicator is the percentage error between the output voltage and the target voltage, calculated as follows: ; In the formula Indicates the first Abnormal indicators corresponding to the group control scheme.

[0044] When an abnormal indicator exceeds a preset threshold, the backup plan is switched sequentially in descending order of score until the abnormal indicator does not exceed the preset threshold. If the abnormal indicator still exceeds the preset threshold after going through all backup plans, the alarm module will issue an alarm.

[0045] In this step, the percentage error between the output voltage and the target voltage is used as an anomaly indicator. This can reflect the deviation between the actual compensation effect of the DVR and the expected target in real time, promptly detect control scheme failures or abnormalities, and also allow the system to quickly switch to a better-performing backup scheme when the main scheme is abnormal, ensuring the continuity and stability of voltage recovery, thereby improving the system's anti-interference and anti-fault capabilities.

[0046] Please see Figure 2 This embodiment also provides a magnetic reactive dynamic voltage recovery method based on particle swarm optimization. The dynamic voltage recovery method is obtained by the aforementioned dynamic voltage recovery device, and the specific steps include: S1: Collect historical voltage dips in the power grid, perform time-series analysis to generate predicted voltage dips, and generate the amplitude range of the injected voltage based on the predicted voltage dips. S2: Detect the temperature and electrical parameter changes of the voltage recovery device body when generating different injection voltages. Combine the detection results with the particle swarm algorithm, use the amplitude range of the injection voltage as the search range, generate several control schemes for the injection voltage, and score them. S3: Sort the control schemes based on their scores and select the optimal scheme and at least one backup scheme from several groups of control schemes; S4: Execute the optimal solution to generate the injection voltage, monitor the injection voltage in real time, generate anomaly indicators based on the measured values ​​of the injection voltage, and switch solutions based on the anomaly indicators.

[0047] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0048] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0049] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0050] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A magnetic resistance type dynamic voltage restorer based on particle swarm optimization, characterized in that, Specifically comprising a control device and a voltage recovery device body, wherein: The control device comprises: A data acquisition module for acquiring historical voltage dips and transmitting them to a time series analysis module; The time series analysis module has a time series analysis model built in, for time series analysis of the historical voltage dips to obtain a predicted voltage dip, and generation of an amplitude interval of an injected voltage based on the predicted voltage dip; An algorithm optimization module comprising a data detection unit, an algorithm optimization unit, and a scheme screening unit, wherein: The data detection unit is used to detect the temperature change and electrical parameter change generated by the voltage recovery device body when generating injected voltages of different amplitudes; The algorithm optimization unit has a particle swarm algorithm built in, for searching within the amplitude interval of the injected voltage and generating a plurality of control schemes of the injected voltage; The scheme screening unit is used to screen an optimal scheme and at least one backup scheme from the plurality of control schemes, and to transmit the optimal scheme and the backup scheme to the voltage recovery device body; A signal feedback module comprising a voltage detection unit and a scheme switching unit, wherein: The voltage detection unit is used to detect the injected voltage generated by the voltage recovery device body in real time, to obtain a measured value of the injected voltage, and to transmit it to the scheme switching unit; The scheme switching unit is used to generate an abnormality index based on the measured value of the injected voltage, and to control the voltage recovery device body to switch the injected voltage scheme when the abnormality index exceeds a preset index threshold; The voltage recovery device body is used to generate injected voltages of different amplitudes by adjusting its magnetic circuit and injected current.

2. The magnetic reluctance based dynamic voltage restoration device based on particle swarm optimization as claimed in claim 1 wherein: The prediction logic of the amplitude interval of the injected voltage is: Data preprocessing is performed on the collected historical voltage dips, including but not limited to denoising, outlier removal, normalization, and formation of a continuous time series data set; Time series analysis is performed on the time series data set formed based on the historical voltage dips, and based on the time series analysis results, a predicted voltage dip and a predicted dip duration are obtained, and the maximum and minimum values of the predicted voltage dip are respectively taken as the upper and lower limits of the amplitude interval of the injected voltage.

3. The magnetic reluctance based dynamic voltage restoration device using particle swarm optimization of claim 2, wherein: The working logic of the algorithm optimization module is: Based on the predicted dip duration, the duration of the injected voltage is determined, and a plurality of control points are uniformly set within the duration, each control point corresponding to an injected voltage of a different amplitude, and all control points together constituting a control scheme of the injected voltage; The algorithm optimization unit generates a control scheme using the particle swarm algorithm, and inputs it to the voltage recovery device body, and detects the temperature change and electrical parameter change generated thereby using the data detection unit, wherein the electrical parameters include but are not limited to the injected current and output voltage of the voltage recovery device body; The algorithm optimization unit scores each control scheme based on the detection results output by the data detection unit, and transmits the control scheme and its corresponding score value to the scheme screening unit; The scheme screening unit arranges the control schemes in order from high to low according to the score values, takes the control scheme ranked first as the optimal scheme, and takes the control schemes ranked second or later as backup schemes.

4. The magnetic reluctance based dynamic voltage restoration device using particle swarm optimization of claim 3, wherein: The algorithm optimization unit scores the control schemes by constructing an objective function; The objective function is obtained based on the detection results output by the data detection unit, and is composed of a voltage error term, a current impact term, an energy loss term, and a temperature rise constraint term, each multiplied by a corresponding normalized weight.

5. The magnetic reluctance based dynamic voltage restoration device using particle swarm optimization of claim 4, wherein: The voltage error term is the reciprocal of the mean square error between the output voltage and the preset target voltage; The current impact term is the reciprocal of the peak value of the injected current; The energy loss term is the reciprocal of the total energy within the duration, which is calculated from the injected voltage, the injected current, and the time interval between two adjacent groups of control points; The temperature rise constraint term is a conditional function, which is a fixed constant when the temperature change in the detection result does not exceed the preset temperature threshold, and its size is negatively correlated with the amount of exceeding when the temperature change in the detection result exceeds the preset temperature threshold.

6. The magnetic reluctance based dynamic voltage restoration device using particle swarm optimization of claim 5, wherein: The abnormality index is the error percentage of the output voltage compared to the target voltage.

7. The magnetic reluctance based dynamic voltage restoration device using particle swarm optimization of claim 6, wherein: The dynamic voltage recovery device further includes an alarm module that switches the backup schemes in order from high to low according to the score values when the abnormality index exceeds the preset index threshold until the abnormality index does not exceed the preset index threshold; if the abnormality index still exceeds the preset index threshold after traversing all backup schemes, the alarm module issues an alarm.

8. A magnetic-reactance based dynamic voltage restoration method based on particle swarm optimization, characterized in that: The dynamic voltage recovery method is performed by the dynamic voltage recovery device of any one of claims 1-7, and the specific steps include: S1: Collecting the historical voltage drop of the power grid, performing time series analysis to generate a predicted voltage drop, and generating an amplitude interval of the injected voltage based on the predicted voltage drop; S2: Detecting the temperature change and electrical parameter change generated by the voltage recovery device when generating different injected voltages, combining the detection results and the particle swarm algorithm, taking the amplitude interval of the injected voltage as the search range, generating a plurality of control schemes of the injected voltage and scoring them; S3: Sorting the control schemes based on their score values, and selecting an optimal scheme and at least one backup scheme from the plurality of control schemes; S4: Executing the optimal scheme to generate the injected voltage, detecting the injected voltage in real time, generating an abnormality index based on the measured value of the injected voltage, and switching the schemes based on the abnormality index.

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