Novel anti-interference electricity and electric energy quality integrated control system

Abstract

The invention relates to the technical field of power electronics, and particularly discloses a novel anti-interference electricity and electric energy quality integrated control system, which is characterized by comprising an anti-interference electricity implementation circuit, an electric energy quality integrated module, a detection and compensation algorithm unit, a hardware resource module and a control module, the detection and compensation algorithm unit collects operation data of a power grid and a load through a voltage sensor and a current sensor, the operation data comprises voltage drop amplitude, harmonic content, reactive vacancy, three-phase current unbalance degree and power grid frequency, and initial detection data is obtained; the control module calls a preset data processing algorithm, performs feature extraction and validity verification on the initial detection data, and eliminates abnormal interference data to obtain accurate operation data; and the control module is also used for carrying out priority division on compensation tasks corresponding to the accurate operation data. By adopting the technical scheme of the invention, the problems of algorithm conflict, capacity contradiction and misjudgment can be solved, and the reliability and the cost performance are improved.

Description

Technical Field

[0001] This invention relates to the field of power electronics, and in particular to a novel integrated control system for power fluctuation and power quality. Background Technology

[0002] During power system operation, voltage fluctuations (short-term voltage drops or sudden descents) and power quality issues (such as harmonic pollution, reactive power imbalance, and three-phase current imbalance) can severely affect the normal operation of electrical equipment. For example, voltage fluctuations in industrial production lines may cause motor shutdowns and production interruptions; excessive harmonics in data centers can cause server power modules to overheat and be damaged; and three-phase imbalances in medical facilities may affect the operational accuracy of precision diagnostic equipment. To address these issues, control systems integrating anti-voltage fluctuation and multi-type power quality management functions have emerged.

[0003] The aforementioned system needs to simultaneously perform functions such as voltage regulation, harmonic compensation, reactive power compensation, imbalance mitigation, and voltage dip mitigation. Compensation commands for these functions may conflict instantaneously, competing for shared hardware resources such as inverter capacity and switching frequency. Due to the lack of effective priority management and coordination strategies, when multiple compensation tasks are triggered simultaneously, the compensation effects can easily weaken each other, and in extreme cases, internal system oscillations may occur. For example, when the power grid experiences both voltage dips and high-order harmonics simultaneously, if the algorithm cannot prioritize resource allocation for voltage dip mitigation, voltage recovery may be delayed, leading to load outages.

[0004] Anti-voltage fluctuation circuits (such as DC bus support circuits) rely on energy storage elements to maintain DC bus voltage stability. The energy storage capacity and power density directly determine the duration of the anti-voltage fluctuation. In existing systems, if the energy storage elements are improperly selected or have insufficient capacity, they cannot continuously support the DC bus voltage under prolonged and severe voltage fluctuation scenarios. This leads to the failure of subsequent power quality compensation algorithms, the loss of system voltage regulation, and ultimately, power outages for electrical equipment.

[0005] Voltage dip mitigation requires systems to reserve significant apparent power and energy capacity to cope with short-term voltage drops. However, this capacity remains idle for extended periods during normal operation, competing with functions that require continuous operation, such as harmonic compensation and reactive power compensation. To accommodate both functions, existing systems need to increase overall capacity, leading to higher equipment costs, larger size, and a significantly reduced cost-effectiveness.

[0006] Existing system detection and compensation algorithms lack sufficient accuracy in identifying grid conditions. They are prone to misinterpreting disturbances caused by normal grid-side operations (such as capacitor switching) or remote faults as local power fluctuations or power quality issues, thus triggering unnecessary compensation actions or even equipment disconnection from the grid, and becoming new sources of grid disturbance. For example, when the grid is performing normal capacitor switching, the system may misinterpret it as an abnormal voltage, activating anti-power fluctuation compensation, causing grid voltage fluctuations and affecting the operation of surrounding electrical equipment.

[0007] The high-power, high-frequency converter is the core working component, but it is also a major source of harmonics and electromagnetic interference. If the output filter is not designed properly, the high-frequency switching subharmonics generated by the converter will be injected into the power grid, causing new power quality pollution. This creates a paradox of pollution control resulting in pollution generation, increasing the burden of harmonic control on the power grid.

[0008] Therefore, there is an urgent need for a new integrated control system for anti-power fluctuations and power quality that can solve problems such as algorithm conflicts, capacity contradictions, and misjudgments, and improve reliability and cost-effectiveness. Summary of the Invention

[0009] This invention provides a novel integrated control system for anti-power fluctuation and power quality, which can solve problems such as algorithm conflicts, capacity contradictions, and misjudgments, and improve reliability and cost-effectiveness.

[0010] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0011] A novel integrated control system for anti-power fluctuation and power quality includes an anti-power fluctuation implementation circuit, a power quality integration module, a detection and compensation algorithm unit, a hardware resource module, and a control module.

[0012] The detection and compensation algorithm unit collects the operating data of the power grid and the load through voltage sensors and current sensors. The operating data includes voltage drop amplitude, harmonic content, reactive power deficit, three-phase current imbalance and power grid frequency to obtain initial detection data.

[0013] The control module invokes a preset data processing algorithm to extract features and verify the validity of the initial detection data, eliminating abnormal interference data to obtain accurate operating data. The control module also prioritizes the compensation tasks corresponding to the accurate operating data, setting the power sag mitigation task as the highest priority, the three-phase imbalance mitigation and harmonic compensation tasks as the second highest priority, and the reactive power compensation and load voltage adjustment tasks as the lowest priority, thus obtaining a task priority allocation result. Furthermore, the control module stores and analyzes historical compensation data to identify the hardware resource competition threshold when different compensation tasks are triggered simultaneously. Before reaching the resource competition threshold, it adjusts the compensation parameters of each compensation task based on the accurate operating data to prevent internal system oscillations. Then, it invokes a preset dynamic resource allocation sub-algorithm, establishing a mathematical model based on the task priority allocation result and the capacity and switching frequency parameters of the hardware resource modules, dynamically allocating the capacity and switching frequency of the hardware resource modules to obtain resource allocation instructions.

[0014] The hardware resource module receives resource allocation instructions and allocates the corresponding capacity and switching frequency to the anti-power fluctuation implementation circuit and the power quality integration module, respectively.

[0015] The anti-power fluctuation implementation circuit and power quality integration module are used to perform corresponding operations according to resource allocation instructions.

[0016] The basic principle and beneficial effects of this invention are as follows: This invention achieves coordinated operation of anti-power dips and multi-dimensional power quality management through a closed-loop logic of layered detection, precise processing, dynamic coordination, and on-demand execution. The detection and compensation algorithm unit relies on voltage and current sensors to collect key operational data of the power grid and load in real time (voltage dip amplitude, harmonic content, reactive power deficit, three-phase current imbalance, and grid frequency), directly obtaining initial detection data reflecting the power grid status and load demand, providing raw input for subsequent processing.

[0017] The control module invokes a preset data processing algorithm to perform dual processing on the initial detection data. On the one hand, it focuses on core parameters through feature extraction (such as extracting the duration of voltage drops, the frequency distribution of harmonics, and the real-time rate of change of reactive power deficit); on the other hand, it filters invalid information through validity verification (such as removing abnormal data caused by instantaneous sensor interference and instantaneous power grid fluctuations), and finally outputs accurate operating data after noise reduction and focusing, ensuring the accuracy of input for subsequent task judgment and resource allocation.

[0018] Based on precise operational data, the control module first prioritizes compensation tasks, setting voltage dips as the highest priority (because short-term voltage drops directly cause load shutdowns, affecting system safety), three-phase imbalance management and harmonic compensation as the second highest priority (because waveform distortion can easily cause equipment overheating and damage, threatening equipment reliability), and reactive power compensation and load voltage adjustment as the lowest priority (because they only affect operating efficiency and have no immediate safety risks), thus clarifying the sequence of task execution. Then, by storing and analyzing historical compensation data (such as resource occupancy rates and compensation effect feedback when multiple tasks are triggered simultaneously), the module identifies critical points of competition for hardware resources (capacity, switching frequency) (e.g., when the inverter capacity occupancy rate reaches 85%, allocation conflicts are likely to occur), and adjusts compensation parameters in advance based on precise operational data (e.g., reducing the instantaneous compensation amplitude of low-priority tasks), thus mitigating the risk of internal system oscillations from the source.

[0019] The control module calls the dynamic resource allocation sub-algorithm, which uses the task priority allocation result as the core basis and combines the actual capacity and switching frequency parameters of the hardware resource module to establish a mathematical model (such as allocating inverter capacity based on priority weight) to calculate the resource allocation instructions that are adapted to the needs of each task. After receiving the instructions, the hardware resource module accurately allocates the corresponding capacity and switching frequency to the anti-power fluctuation circuit (performing power fluctuation control) and the power quality integration module (performing voltage adjustment, harmonic compensation, etc.). Finally, the two major functional modules execute the operations as needed to complete the closed loop of data, decision-making and execution.

[0020] To address the issue of algorithmic instruction conflicts that can easily occur when multiple tasks are triggered simultaneously (such as power sag mitigation and harmonic compensation competing for inverter capacity), this invention employs a dual mechanism of priority allocation and conflict prediction. First, it clarifies the task execution order, ensuring that high-priority power sag mitigation receives resources first. Then, it identifies conflict thresholds using historical data and adjusts compensation parameters in advance (e.g., temporarily reducing the switching frequency occupancy rate of harmonic compensation when power sag occurs), preventing interference between compensation instructions from different tasks. This fundamentally resolves algorithmic conflicts, eliminates internal system oscillations, and improves system stability.

[0021] To balance voltage fluctuation mitigation (requiring substantial reserved capacity) and routine power quality management (requiring continuous capacity utilization), excessive hardware capacity design is necessary, leading to high costs and large size. This invention addresses this by dynamically allocating resources. During normal operation, hardware capacity is prioritized for routine tasks such as harmonic compensation and reactive power compensation (higher / lower priority). Capacity required for voltage fluctuation mitigation is only activated via command during voltage dips. Furthermore, precise capacity requirements are calculated using a mathematical model, avoiding excessive reservations. This improves hardware capacity utilization, reduces equipment costs, shrinks size, and significantly enhances cost-effectiveness.

[0022] To address the issue that relying on single detection data (such as judging only the magnitude of voltage drops) can easily lead to misjudging normal grid operations (such as capacitor switching) as voltage fluctuations, this invention solves the problem by combining data validity verification with support from accurate operational data. During the data processing stage, transient interference data generated by normal grid operations is removed. Subsequent task judgments and resource allocation are based on the denoised, accurate operational data, thereby improving the accuracy of grid status identification, reducing the false alarm rate, and preventing the system from becoming a new source of grid disturbance.

[0023] To address the problem of hardware modules (such as inverters and sensors) being overloaded and damaged due to complex structures and disordered task competition for resources, this invention solves the problem through precise data input and dynamic resource allocation. On the one hand, precise operation data avoids invalid instructions triggering hardware idling; on the other hand, resources are dynamically allocated according to priority to prevent hardware from overloading due to multiple tasks running at full capacity simultaneously. This reduces the failure rate of hardware modules, extends the service life of equipment by 1-2 years, and improves the overall reliability of the system.

[0024] The stored historical compensation data can provide real-time feedback on the operational status of each task (such as the monthly average effect of harmonic compensation and the annual trigger count of power sloshing control), providing maintenance personnel with accurate maintenance basis (such as determining whether filters need to be replaced based on harmonic compensation data, and identifying weak links in the power grid based on power sloshing trigger data), avoiding blind maintenance; at the same time, dynamic resource allocation instructions can be visualized and output through the control module, making it easy for maintenance personnel to monitor resource occupancy in real time, improving maintenance efficiency and reducing annual maintenance costs.

[0025] This invention reduces power grid waveform distortion and power loss through precise harmonic compensation, reactive power compensation, and three-phase imbalance management, thereby improving power quality on the load side. At the same time, the improved power factor can reduce line losses (line losses are inversely proportional to the square of the power factor), thus combining energy saving and compliance value.

[0026] In summary, this invention improves reliability and cost-effectiveness by resolving issues such as algorithm conflicts, capacity discrepancies, and misjudgments.

[0027] Furthermore, when the detection and compensation algorithm unit collects operational data, it defines the voltage drop amplitude as... Using formula Calculation, where This is the rated voltage of the power grid. To detect the real-time voltage of the power grid at the specified time; and only when Only when this parameter is recorded in the initial detection data is it used to distinguish between normal power grid fluctuations and early signs of voltage dips, so as to avoid triggering subsequent compensation processes due to minor voltage fluctuations.

[0028] The harmonic content is defined as The power grid current signal is decomposed using Fast Fourier Transform (FFT) to extract higher harmonic components such as the 3rd, 5th, and 7th harmonics. The formula is then used... Calculate the total harmonic distortion rate, where This is the effective value of the fundamental current. The effective value of the nth harmonic current is given; the values ​​of each harmonic component are simultaneously recorded in the initial detection data, which not only meets the judgment requirements of total harmonic mitigation, but also provides data support for targeted compensation of specific high-order harmonics.

[0029] Define reactive power deficit as Using formula Calculation, where For the active power of the load, The current power factor angle, The target power factor angle; simultaneously, the current power factor Recording the initial test data enables both accurate calculation of reactive power compensation and... and The difference is used to predict the compensation effect and avoid over-compensation or under-compensation.

[0030] Define the three-phase imbalance as Using formula Calculation, where , , These represent the maximum, minimum, and average values ​​of the three-phase voltages, respectively; the instantaneous values ​​of the three-phase voltages are also synchronized. , , Record the initial test data to provide the original data basis for the precise phase adjustment of subsequent three-phase imbalance treatment;

[0031] Define the power grid frequency as Real-time frequency values ​​are collected via a frequency sensor, and the normal frequency threshold is set to 49.5Hz~50.5Hz; only when Exceeding this threshold or frequency change rate At that time, the frequency data is marked as needing attention and recorded in the initial detection data to avoid erroneously starting the anti-power fluctuation process due to normal frequency fluctuations;

[0032] The detection and compensation algorithm unit will use the parameters defined above after standardization. , and each harmonic component, and , and , The frequency change rate is packaged into initial detection data in the format of parameter name-calculation result-collection timestamp and synchronously stored in the historical database of the control module.

[0033] Furthermore, the preset data processing algorithm invoked by the control module includes an abnormal data identification sub-algorithm and a feature enhancement sub-algorithm;

[0034] The anomaly data identification sub-algorithm adopts an outlier removal model based on the 3σ principle, and the formula is: ,in:

[0035] For any parameter value in the initial detection data;

[0036] This is the average value of the parameter collected over the last 10 minutes;

[0037] This is the standard deviation of the parameter's data collected over the last 10 minutes.

[0038] When satisfied If the data is found to be abnormal or interfering, it will be removed.

[0039] The feature enhancement sub-algorithm reconstructs features from the remaining valid data, generating feature values ​​representing parameter change trends. The formula is: ,in:

[0040] The parameter value at the current time. The parameter value is from the previous acquisition time.

[0041] This refers to the data collection time interval;

[0042] Ultimately, the effective parameter values ​​and trend characteristic values ​​will be... Encapsulated into precise operational data;

[0043] Furthermore, when prioritizing compensation tasks corresponding to precise operational data, the control module introduces a dynamic priority correction coefficient. Corrected task priority weights The calculation formula is: ,in:

[0044] Based on priority weights, among which, power sag management Three-phase imbalance control and harmonic compensation Reactive power compensation and load voltage regulation ;

[0045] This is a dynamic correction coefficient, whose value is determined by the characteristic values ​​of parameter change trends in the precise operational data. Decision: When When the parameter deterioration rate is fast, ;when hour, ;when hour, .

[0046] Furthermore, when the control module identifies a critical point of hardware resource contention, it establishes a resource contention risk assessment model. The formula is: ,in:

[0047] The number of compensation tasks triggered simultaneously;

[0048] For the first The revised priority weights for each task;

[0049] For the first The amount of hardware resources required for each task;

[0050] Rated total capacity of hardware resource modules / rated maximum switching frequency;

[0051] Set a resource contention threshold ,when The critical point is determined at that time.

[0052] The above content is passed through and The coupled calculation takes into account both the weight differences of task priorities (high-priority tasks contribute more to risk) and actual resource requirements, avoiding the one-sidedness of judging the critical point solely based on priority or resource quantity, thus improving the accuracy of critical point identification and providing a quantitative basis for subsequent parameter adjustments.

[0053] Furthermore, when the control module adjusts the compensation parameters for each compensation task based on precise operational data, it employs a dynamic adjustment model for the compensation parameters. The formula is: ,in:

[0054] For the first Initial compensation parameters for each task;

[0055] This is the current assessment value of resource competition risk;

[0056] The resource security threshold is set to 0.6; no parameter adjustment is needed if the value is below this.

[0057] This represents the threshold value for the critical point in resource competition. ;

[0058] when When doing so, reduce the priority of low-priority tasks according to the above formula. High-priority tasks Keep constant.

[0059] The above content is passed through and The associated calculation enables on-demand adjustment of compensation parameters, ensuring that the compensation effect of high-priority tasks is not affected, while reducing the parameters of low-priority tasks avoids resource overload. At the same time, it avoids compensation failure caused by excessive parameter adjustment, achieving reduction without failure and ensuring that the compensation effect is maintained at a high level in resource contention scenarios.

[0060] Furthermore, in the preset dynamic resource allocation sub-algorithm invoked by the control module, the hardware resource capacity allocation model is as follows: ,in:

[0061] To be assigned to the Hardware capacity for each task;

[0062] This refers to the rated total capacity of the hardware resource modules;

[0063] For the first The revised priority weights for each task;

[0064] For the first The urgency coefficient of each task is determined by the characteristic values ​​of parameter changes in precise operational data. Decide: hour , hour , hour ;

[0065] This represents the total number of tasks that currently require resource allocation.

[0066] The switching frequency allocation model is ,in:

[0067] To be assigned to the The switching frequency of each task;

[0068] This refers to the rated maximum switching frequency of the hardware resource module.

[0069] For the first Response time requirement coefficient for each task: Power sloshing mitigation Rapid response is required; three-phase imbalance management and harmonic compensation are needed. Reactive power compensation and load voltage regulation .

[0070] The above content is passed through , (or The multi-dimensional weighting ensures that high-priority tasks receive sufficient capacity and high-frequency support, while also optimizing allocation based on task urgency and response requirements, thereby improving resource utilization and meeting the response speed requirements of different tasks.

[0071] Furthermore, after generating resource allocation instructions, the control module employs an instruction feasibility verification formula. ,in:

[0072] For the first Minimum capacity requirement for each task;

[0073] For the first Minimum switching frequency requirement for each task;

[0074] , To be assigned to the The capacity and switching frequency of each task;

[0075] when At that time, the instruction verification submodule triggers a secondary resource allocation: prioritizing and reducing the priority of low-priority tasks. and , This is a low-priority task, added to In the task, until all tasks are satisfied .

[0076] This verification mechanism both... The value quantification of instruction feasibility avoids task failure due to insufficient allocation (such as insufficient capacity for power fluctuation control leading to voltage failure), and ensures the minimum requirements of high-priority tasks through secondary allocation, while avoiding excessive waste of resources, thereby improving the success rate of instruction execution and further ensuring the reliability of system operation.

[0077] Furthermore, the hardware resource module includes two parallel three-phase full-bridge inverters and a switching frequency regulation unit;

[0078] The anti-power fluctuation circuit includes a supercapacitor energy storage unit, a bidirectional DC-DC converter module, a DC bus voltage monitoring module, and an overcurrent protection module.

[0079] After receiving the power fluctuation mitigation capacity and switching frequency allocated by the hardware resource module, the bidirectional DC-DC converter module first obtains the current DC bus voltage value through the DC bus voltage monitoring module and compares it with the bus stable voltage required to resist power fluctuation.

[0080] When the grid voltage drops to ≥5%, the bidirectional DC-DC converter module adjusts the energy release rate according to the allocated capacity, and controls the internal IGBTs to turn on and off according to the allocated switching frequency, converting the electrical energy of the supercapacitor energy storage unit into stable DC power to maintain the stability of the DC bus voltage.

[0081] The overcurrent protection module monitors the output current of the bidirectional DC-DC converter module in real time. When the current exceeds the rated current, the energy output circuit is immediately cut off.

[0082] Furthermore, the power quality integrated module includes a voltage adjustment submodule, a harmonic compensation submodule, a reactive power compensation submodule, and a three-phase imbalance mitigation submodule;

[0083] Voltage regulation submodule: includes an autotransformer and a thyristor control module. After receiving the capacity allocated by the hardware resource module, the thyristor control module adjusts the conduction angle according to the capacity to control the output voltage of the autotransformer and ensure the stability of the load voltage.

[0084] Harmonic compensation submodule: including active power filter and LC passive filter branch. After receiving the capacity and switching frequency allocated by the hardware resource module, the APF detects the harmonic content of the power grid in real time according to the allocated switching frequency, and outputs compensation current according to the allocated capacity. Combined with the LC passive filter branch, it specifically suppresses the 3rd and 5th high-order harmonics, so that the total harmonic distortion rate is reduced to below 5%, avoiding overheating and damage to the motor windings caused by harmonics.

[0085] Reactive power compensation submodule: includes group switching capacitor banks and reactive power detection module. After receiving the capacity allocated by the hardware resource module, it controls the number of group switching capacitor banks according to the capacity requirements to realize reactive power compensation and improve the power factor from 0.8 to above 0.95.

[0086] The three-phase imbalance management submodule includes a three-phase series reactor and a current balance monitoring module. After receiving the capacity allocated by the hardware resource module, it adjusts the inductance value of the three-phase series reactor according to the capacity size to balance the three-phase current and reduce the three-phase imbalance to a preset range. This prevents the unbalanced current from causing local overheating of the distribution transformer and extends the service life of the transformer. Attached Figure Description

[0087] Figure 1 This is a logic block diagram of a novel integrated control system for anti-power fluctuation and power quality.

[0088] Figure 2 This is a logic block diagram related to the hardware resource modules in a novel integrated control system for anti-power fluctuation and power quality.

[0089] Figure 3 This is a logic block diagram related to the power quality integration module in a novel integrated control system for anti-power fluctuation and power quality. Detailed Implementation

[0090] The following detailed description illustrates the specific implementation methods:

[0091] A novel integrated control system for anti-power fluctuation and power quality (such as...) Figure 1 As shown, it includes an anti-power fluctuation implementation circuit, a power quality integration module, a detection and compensation algorithm unit, a hardware resource module, and a control module;

[0092] The detection and compensation algorithm unit collects the operating data of the power grid and the load through voltage sensors and current sensors. The operating data includes voltage drop amplitude, harmonic content, reactive power deficit, three-phase current imbalance and power grid frequency to obtain initial detection data.

[0093] The control module invokes a preset data processing algorithm to extract features and verify the validity of the initial detection data, eliminating abnormal interference data to obtain accurate operating data. The control module also prioritizes the compensation tasks corresponding to the accurate operating data, setting the power sag mitigation task as the highest priority, the three-phase imbalance mitigation and harmonic compensation tasks as the second highest priority, and the reactive power compensation and load voltage adjustment tasks as the lowest priority, thus obtaining a task priority allocation result. Furthermore, the control module stores and analyzes historical compensation data to identify the hardware resource competition threshold when different compensation tasks are triggered simultaneously. Before reaching the resource competition threshold, it adjusts the compensation parameters of each compensation task based on the accurate operating data to prevent internal system oscillations. Then, it invokes a preset dynamic resource allocation sub-algorithm, establishing a mathematical model based on the task priority allocation result and the capacity and switching frequency parameters of the hardware resource modules, dynamically allocating the capacity and switching frequency of the hardware resource modules to obtain resource allocation instructions.

[0094] The hardware resource module receives resource allocation instructions and allocates the corresponding capacity and switching frequency to the anti-power fluctuation implementation circuit and the power quality integration module, respectively.

[0095] The anti-power fluctuation implementation circuit and power quality integration module are used to perform corresponding operations according to resource allocation instructions.

[0096] In practical application, this embodiment is used as an example of an industrial production line power supply system (the load includes CNC machine tools, conveyor motors, etc., with a rated voltage of 380V and a total load power of 800kVA) for detailed explanation.

[0097] Specifically, the hardware resource module includes two parallel three-phase full-bridge inverters (each with a rated capacity of 50kVA and a total rated capacity of 100kVA) and an FPGA switching frequency control unit (supporting a switching frequency adjustment range of 5kHz-20kHz).

[0098] The anti-voltage fluctuation circuit consists of a supercapacitor energy storage unit (rated voltage 600V, capacity 100F), a bidirectional DC-DC converter module (rated capacity 35kVA), a DC bus voltage monitoring module (sampling accuracy ±0.5%), and an overcurrent protection module (rated protection current 80A).

[0099] The power quality integrated module includes a voltage regulation submodule (autotransformer, rated capacity 30kVA), a harmonic compensation submodule (APF rated capacity 35kVA + LC passive filter branch), a reactive power compensation submodule (grouped switching capacitor banks, single group capacity 10kvar, 6 groups in total), and a three-phase imbalance control submodule (three-phase series reactor, rated capacity 25kVA).

[0100] The detection and compensation algorithm unit is equipped with a voltage sensor (measurement range 0-500V, accuracy class 0.2) and a current sensor (measurement range 0-500A, accuracy class 0.2) to collect operating data of the power grid and load;

[0101] The control module uses a DSP chip (model TMS320F28335) and has built-in preset data processing algorithms, dynamic resource allocation sub-algorithms and conflict prediction logic.

[0102] When the detection and compensation algorithm unit collects operational data, it defines the voltage drop amplitude as... Using formula Calculation, where This is the rated voltage of the power grid. To detect the real-time voltage of the power grid at the specified time; and only when Only when this parameter is recorded in the initial detection data is it used to distinguish between normal power grid fluctuations and early signs of voltage dips, so as to avoid triggering subsequent compensation processes due to minor voltage fluctuations.

[0103] The harmonic content is defined as The power grid current signal is decomposed using Fast Fourier Transform (FFT) to extract higher harmonic components such as the 3rd, 5th, and 7th harmonics. The formula is then used... Calculate the total harmonic distortion rate, where This is the effective value of the fundamental current. The effective value of the nth harmonic current is given; the values ​​of each harmonic component are simultaneously recorded in the initial detection data, which not only meets the judgment requirements of total harmonic mitigation, but also provides data support for targeted compensation of specific high-order harmonics.

[0104] Define reactive power deficit as Using formula Calculation, where For the active power of the load, The current power factor angle, The target power factor angle; simultaneously, the current power factor Recording the initial test data enables both accurate calculation of reactive power compensation and... and The difference is used to predict the compensation effect and avoid over-compensation or under-compensation.

[0105] Define the three-phase imbalance as Using formula Calculation, where , , These represent the maximum, minimum, and average values ​​of the three-phase voltages, respectively; the instantaneous values ​​of the three-phase voltages are also synchronized. , , Record the initial test data to provide the original data basis for the precise phase adjustment of subsequent three-phase imbalance treatment;

[0106] Define the power grid frequency as Real-time frequency values ​​are collected via a frequency sensor, and the normal frequency threshold is set to 49.5Hz~50.5Hz; only when Exceeding this threshold or frequency change rate At that time, the frequency data is marked as needing attention and recorded in the initial detection data to avoid erroneously starting the anti-power fluctuation process due to normal frequency fluctuations;

[0107] The detection and compensation algorithm unit will use the parameters defined above after standardization. , and each harmonic component, and , and , The frequency change rate is packaged into initial detection data in the format of parameter name-calculation result-collection timestamp and synchronously stored in the historical database of the control module.

[0108] The preset data processing algorithms invoked by the control module include an abnormal data identification sub-algorithm and a feature enhancement sub-algorithm;

[0109] The anomaly data identification sub-algorithm adopts an outlier removal model based on the 3σ principle, and the formula is: ,in:

[0110] For any parameter value in the initial detection data;

[0111] This is the average value of the parameter collected over the last 10 minutes;

[0112] This is the standard deviation of the parameter's data collected over the last 10 minutes.

[0113] When satisfied If the data is found to be abnormal or interfering, it will be removed.

[0114] The feature enhancement sub-algorithm reconstructs features from the remaining valid data, generating feature values ​​representing parameter change trends. The formula is: ,in:

[0115] The parameter value at the current time. The parameter value is from the previous acquisition time.

[0116] This refers to the data collection time interval;

[0117] Ultimately, the effective parameter values ​​and trend characteristic values ​​will be... Encapsulated into precise operational data;

[0118] When the control module prioritizes the compensation tasks corresponding to the precise operational data, it introduces a dynamic priority correction coefficient. Corrected task priority weights The calculation formula is: ,in:

[0119] Based on priority weights, among which, power sag management Three-phase imbalance control and harmonic compensation Reactive power compensation and load voltage regulation ;

[0120] This is a dynamic correction coefficient, whose value is determined by the characteristic values ​​of parameter change trends in the precise operational data. Decision: When When the parameter deterioration rate is fast, ;when hour, ;when hour, ;

[0121] When the control module identifies the critical point of hardware resource contention, it establishes a resource contention risk assessment model. The formula is: ,in:

[0122] The number of compensation tasks triggered simultaneously;

[0123] For the first The revised priority weights for each task;

[0124] For the first The amount of hardware resources required for each task;

[0125] Rated total capacity of hardware resource modules / rated maximum switching frequency;

[0126] Set a resource contention threshold ,when The critical point is determined at that time.

[0127] The above content is passed through and The coupled calculation takes into account both the weight differences of task priorities (high-priority tasks contribute more to risk) and actual resource requirements, avoiding the one-sidedness of judging the critical point solely based on priority or resource quantity, thus improving the accuracy of critical point identification and providing a quantitative basis for subsequent parameter adjustments.

[0128] When the control module adjusts the compensation parameters of each compensation task based on accurate operational data, it employs a dynamic adjustment model for the compensation parameters. The formula is: ,in:

[0129] For the first Initial compensation parameters for each task;

[0130] This is the current assessment value of resource competition risk;

[0131] The resource security threshold is set to 0.6; no parameter adjustment is needed if the value is below this.

[0132] This represents the threshold value for the critical point in resource competition. ;

[0133] when When doing so, reduce the priority of low-priority tasks according to the above formula. High-priority tasks Keep constant.

[0134] The above content is passed through and The associated calculation enables on-demand adjustment of compensation parameters, ensuring that the compensation effect of high-priority tasks is not affected, while reducing the parameters of low-priority tasks avoids resource overload. At the same time, it avoids compensation failure caused by excessive parameter adjustment, achieving reduction without failure and ensuring that the compensation effect is maintained at a high level in resource contention scenarios.

[0135] In the preset dynamic resource allocation sub-algorithm invoked by the control module, the hardware resource capacity allocation model is as follows: ,in:

[0136] To be assigned to the Hardware capacity for each task;

[0137] This refers to the rated total capacity of the hardware resource modules;

[0138] For the first The revised priority weights for each task;

[0139] For the first The urgency coefficient of each task is determined by the characteristic values ​​of parameter changes in precise operational data. Decide: hour , hour , hour ;

[0140] This represents the total number of tasks that currently require resource allocation.

[0141] The switching frequency allocation model is ,in:

[0142] To be assigned to the The switching frequency of each task;

[0143] This refers to the rated maximum switching frequency of the hardware resource module.

[0144] For the first Response time requirement coefficient for each task: Power sloshing mitigation Rapid response is required; three-phase imbalance management and harmonic compensation are needed. Reactive power compensation and load voltage regulation .

[0145] The above content is passed through , (or The multi-dimensional weighting ensures that high-priority tasks receive sufficient capacity and high-frequency support, while also optimizing allocation based on task urgency and response requirements, thereby improving resource utilization and meeting the response speed requirements of different tasks.

[0146] After generating resource allocation instructions, the control module uses an instruction feasibility verification formula. ,in:

[0147] For the first Minimum capacity requirement for each task;

[0148] For the first Minimum switching frequency requirement for each task;

[0149] , To be assigned to the The capacity and switching frequency of each task;

[0150] when At that time, the instruction verification submodule triggers a secondary resource allocation: prioritizing and reducing the priority of low-priority tasks. and , This is a low-priority task, added to In the task, until all tasks are satisfied .

[0151] This verification mechanism both... The value quantification of instruction feasibility avoids task failure due to insufficient allocation (such as insufficient capacity for power fluctuation control leading to voltage failure), and ensures the minimum requirements of high-priority tasks through secondary allocation, while avoiding excessive waste of resources, thereby improving the success rate of instruction execution and further ensuring the reliability of system operation.

[0152] The hardware resource module (such as) Figure 2 (As shown) It includes two parallel three-phase full-bridge inverters and a switching frequency regulation unit;

[0153] The anti-power fluctuation circuit includes a supercapacitor energy storage unit, a bidirectional DC-DC converter module, a DC bus voltage monitoring module, and an overcurrent protection module.

[0154] After receiving the power fluctuation mitigation capacity and switching frequency allocated by the hardware resource module, the bidirectional DC-DC converter module first obtains the current DC bus voltage value through the DC bus voltage monitoring module and compares it with the bus stable voltage required to resist power fluctuation.

[0155] When the grid voltage drops to ≥5%, the bidirectional DC-DC converter module adjusts the energy release rate according to the allocated capacity, and controls the internal IGBTs to turn on and off according to the allocated switching frequency, converting the electrical energy of the supercapacitor energy storage unit into stable DC power to maintain the stability of the DC bus voltage.

[0156] The overcurrent protection module monitors the output current of the bidirectional DC-DC converter module in real time. When the current exceeds the rated current, the energy output circuit is immediately cut off.

[0157] The power quality integrated module (such as) Figure 3 (As shown) It includes a voltage regulation submodule, a harmonic compensation submodule, a reactive power compensation submodule, and a three-phase imbalance mitigation submodule;

[0158] Voltage regulation submodule: includes an autotransformer and a thyristor control module. After receiving the capacity allocated by the hardware resource module, the thyristor control module adjusts the conduction angle according to the capacity to control the output voltage of the autotransformer and ensure the stability of the load voltage.

[0159] Harmonic compensation submodule: including active power filter and LC passive filter branch. After receiving the capacity and switching frequency allocated by the hardware resource module, the APF detects the harmonic content of the power grid in real time according to the allocated switching frequency, and outputs compensation current according to the allocated capacity. Combined with the LC passive filter branch, it specifically suppresses the 3rd and 5th high-order harmonics, so that the total harmonic distortion rate is reduced to below 5%, avoiding overheating and damage to the motor windings caused by harmonics.

[0160] Reactive power compensation submodule: includes group switching capacitor banks and reactive power detection module. After receiving the capacity allocated by the hardware resource module, it controls the number of group switching capacitor banks according to the capacity requirements to realize reactive power compensation and improve the power factor from 0.8 to above 0.95.

[0161] The three-phase imbalance management submodule includes a three-phase series reactor and a current balance monitoring module. After receiving the capacity allocated by the hardware resource module, it adjusts the inductance value of the three-phase series reactor according to the capacity size to balance the three-phase current and reduce the three-phase imbalance to a preset range. This prevents the unbalanced current from causing local overheating of the distribution transformer and extends the service life of the transformer.

[0162] In practical use, initial detection data acquisition is performed first. The detection and compensation algorithm unit collects data in real time through sensors and processes it according to standardized definitions.

[0163] The voltage drop amplitude ΔU, the grid rated voltage U0 = 380V, and the real-time voltage U at a certain moment. t =350V, assuming ΔU≥5%, record this parameter;

[0164] The harmonic content (THD) was calculated by decomposing the current signal using FFT. The fundamental current I1 = 120A, the 3rd harmonic I3 = 15A, and the 5th harmonic I5 = 8A. Assuming the calculated THD ≈ 13.89%, each harmonic component was recorded simultaneously.

[0165] Given a reactive power deficit of Q0, a load active power of P = 600kW, a current power factor angle of φ1 = 31° (cosφ1 = 0.86), a target power factor angle of φ2 = 18° (cosφ2 = 0.95), and a calculated Q0 = 165kvar;

[0166] Three-phase unbalance ε, assuming three-phase voltage U A =382V, U B =375V, U C =383V, U max =383V, U min =375V, U avg =380V, assuming ε≈2.11%;

[0167] The power grid frequency f, assuming a collected value of 50.2Hz, is within the normal threshold of 49.5Hz-50.5Hz and does not require marking or attention.

[0168] The above parameters are packaged into initial detection data according to parameter name-calculation result-collection timestamp (e.g., 2025-10-2014:30:00.000) and stored in the historical database of the control module.

[0169] Then, the control module calls the data processing algorithm to identify abnormal data. It takes the average value of ΔU collected in the last 10 minutes as x̄=3.2%, standard deviation σ=1.5%, and the current ΔU=7.89%, and assumes it is valid data (non-abnormal).

[0170] Feature enhancement: Assume the characteristic value of the change trend of ΔU is calculated as k = 6.9% / s (assuming ΔU = 7.2% at the previous moment and the acquisition interval is 0.1s).

[0171] Finally, precise operational data such as ΔU and THD are encapsulated.

[0172] Then, task priorities are assigned, and a dynamic correction coefficient α is introduced. Assuming that k > 0.5% / s (preset value) for ΔU, then α = 0.2.

[0173] Therefore, we can assume that the basic weight for power sag control is W0=0.4, and after correction W=0.48; the basic weight for harmonic compensation is W0=0.3, and after correction W=0.36; and the basic weight for reactive power compensation is W0=0.2, and after correction W=0.24.

[0174] The task priorities are: power sloshing control (0.48) > harmonic compensation (0.36) > reactive power compensation (0.24).

[0175] When identifying the critical point of resource competition, it is assumed that power sag control (S1=30kVA), harmonic compensation (S2=25kVA), and reactive power compensation (S3=15kVA) are triggered simultaneously, and the total hardware capacity S_max=100kVA; it is assumed that the calculated risk assessment value R<0.85, and the critical point has not been reached.

[0176] When adjusting compensation parameters and allocating resources, if R < 0.6 (safety threshold), no parameter adjustment is required.

[0177] The capacity allocation logic is as follows: assume power sag control D1=1.2, harmonic compensation D2=1.2, and reactive power compensation D3=1.2.

[0178] Assume the voltage sag control capacity is C1 = 48 kVA; harmonic compensation capacity is C2 = 36 kVA; reactive power compensation capacity is C3 = 24 kVA;

[0179] The logic for switching frequency allocation is as follows: assuming power sag control T1=1.5, harmonic compensation T2=1.2, reactive power compensation T3=1.0, and the total switching frequency f... max =20kHz; Assuming the frequency for controlling voltage fluctuations is f1≈10.34kHz; and the frequency for harmonic compensation is f2≈6.22kHz;

[0180] The reactive power compensation frequency f3≈3.45kHz;

[0181] Then, a resource allocation instruction is generated and verified. Assuming the power slump control F≈2.06 and the power slump control F≥0.9, the instruction is valid.

[0182] In the anti-power fluctuation circuit, the bidirectional DC-DC module is designed with a capacity of 48kVA and a frequency of 10.34kHz. Assuming the calculated U... dc ≈598.85V, to maintain stable bus voltage and avoid production line shutdown;

[0183] The voltage regulation submodule, assuming a 36kVA capacity and a conduction angle θ=112°, stabilizes the output voltage at 380V±2%.

[0184] The harmonic compensation submodule, assuming the APF has a capacity of 36kVA and a frequency of 6.22kHz, outputs I... 谐补 ≈0.026A, combined with the LC branch, the THD will be reduced;

[0185] The reactive power compensation submodule, assuming two sets of capacitors are switched on and off with a capacity of 24kVA, improves the power factor;

[0186] The three-phase imbalance mitigation submodule assumes that the inductance value L of the reactor is adjusted. 平 ≈0.71H, which reduces ε.

[0187] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A novel integrated control system for anti-power fluctuation and power quality, characterized in that, It includes an anti-power fluctuation circuit, a power quality integration module, a detection and compensation algorithm unit, a hardware resource module, and a control module; The detection and compensation algorithm unit collects the operating data of the power grid and the load through voltage sensors and current sensors. The operating data includes voltage drop amplitude, harmonic content, reactive power deficit, three-phase current imbalance and power grid frequency to obtain initial detection data. The control module invokes a preset data processing algorithm to extract features and verify the validity of the initial detection data, eliminating abnormal interference data to obtain accurate operating data. The control module also prioritizes the compensation tasks corresponding to the accurate operating data, setting the power sag mitigation task as the highest priority, the three-phase imbalance mitigation and harmonic compensation tasks as the second highest priority, and the reactive power compensation and load voltage adjustment tasks as the lowest priority, thus obtaining a task priority allocation result. Furthermore, the control module stores and analyzes historical compensation data to identify the hardware resource competition threshold when different compensation tasks are triggered simultaneously. Before reaching the resource competition threshold, it adjusts the compensation parameters of each compensation task based on the accurate operating data to prevent internal system oscillations. Then, it invokes a preset dynamic resource allocation sub-algorithm, establishing a mathematical model based on the task priority allocation result and the capacity and switching frequency parameters of the hardware resource modules, dynamically allocating the capacity and switching frequency of the hardware resource modules to obtain resource allocation instructions. The hardware resource module receives resource allocation instructions and allocates the corresponding capacity and switching frequency to the anti-power fluctuation implementation circuit and the power quality integration module, respectively. The anti-power fluctuation implementation circuit and power quality integration module are used to perform corresponding operations according to resource allocation instructions.

2. The novel integrated control system for anti-power fluctuation and power quality according to claim 1, characterized in that, When the detection and compensation algorithm unit collects operational data, it defines the voltage drop amplitude as... Using formula Calculation, where This is the rated voltage of the power grid. To detect the real-time voltage of the power grid at the specified time; and only when Only when this parameter is recorded in the initial detection data is it used to distinguish between normal power grid fluctuations and early signs of voltage dips, so as to avoid triggering subsequent compensation processes due to minor voltage fluctuations. The harmonic content is defined as The power grid current signal is decomposed using Fast Fourier Transform (FFT) to extract the 3rd, 5th, and 7th higher harmonic components, and the formula is used. Calculate the total harmonic distortion rate, where This is the effective value of the fundamental current. This represents the effective value of the nth harmonic current. Simultaneously recording the values ​​of each harmonic component into the initial detection data not only meets the judgment requirements for total harmonic mitigation but also provides data support for targeted compensation of specific higher harmonics. Define reactive power deficit as Using formula Calculation, where For the active power of the load, The current power factor angle, The target power factor angle; simultaneously, the current power factor Recording the initial test data enables both accurate calculation of reactive power compensation and... and The difference is used to predict the compensation effect and avoid over-compensation or under-compensation. Define the three-phase imbalance as Using formula Calculation, where , , These represent the maximum, minimum, and average values ​​of the three-phase voltages, respectively; the instantaneous values ​​of the three-phase voltages are also synchronized. , , Record the initial test data to provide the original data basis for the precise phase adjustment of subsequent three-phase imbalance treatment; Define the power grid frequency as Real-time frequency values ​​are collected via a frequency sensor, and the normal frequency threshold is set to 49.5Hz~50.5Hz; only when Exceeding this threshold or frequency change rate At that time, the frequency data is marked as needing attention and recorded in the initial detection data to avoid erroneously starting the anti-power fluctuation process due to normal frequency fluctuations; The detection and compensation algorithm unit will use the parameters defined above after standardization. , and each harmonic component, and , and , The frequency change rate is packaged into initial detection data in the format of parameter name-calculation result-collection timestamp and synchronously stored in the historical database of the control module.

3. The novel integrated control system for anti-power fluctuation and power quality according to claim 2, characterized in that, The preset data processing algorithms invoked by the control module include an anomaly data identification sub-algorithm and a feature enhancement sub-algorithm; The anomaly data identification sub-algorithm adopts an outlier removal model based on the 3σ principle, and the formula is: ,in: For any parameter value in the initial detection data; This is the average value of the parameter collected over the last 10 minutes; This is the standard deviation of the parameter's data collected over the last 10 minutes. When satisfied If the data is found to be abnormal or interfering, it will be removed. The feature enhancement sub-algorithm reconstructs features from the remaining valid data, generating feature values ​​representing parameter change trends. The formula is: ,in: The parameter value at the current time. The parameter value is from the previous acquisition time. This is the time interval for data collection. Ultimately, the effective parameter values ​​and trend characteristic values ​​will be... Encapsulated into precise operational data; When the control module prioritizes the compensation tasks corresponding to the precise operational data, it introduces a dynamic priority correction coefficient. Corrected task priority weights The calculation formula is: ,in: Based on priority weights, among which, power sag management Three-phase imbalance control and harmonic compensation Reactive power compensation and load voltage regulation ; This is a dynamic correction coefficient, whose value is determined by the characteristic values ​​of parameter change trends in the precise operational data. Decision: When When the parameter deterioration rate is fast, ;when hour, ;when hour, .

4. The novel integrated control system for anti-power fluctuation and power quality according to claim 3, characterized in that, When the control module identifies the critical point of hardware resource contention, it establishes a resource contention risk assessment model. The formula is: ,in: The number of compensation tasks triggered simultaneously; For the first The revised priority weights for each task; For the first The amount of hardware resources required for each task; Rated total capacity of hardware resource modules / rated maximum switching frequency; Set a resource contention threshold ,when The critical point is determined at that time.

5. A novel integrated control system for anti-power fluctuation and power quality according to claim 4, characterized in that, When the control module adjusts the compensation parameters of each compensation task based on accurate operational data, it employs a dynamic adjustment model for the compensation parameters. The formula is: ,in: For the first Initial compensation parameters for each task; This is the current assessment value of resource competition risk; The resource security threshold is set to 0.6; no parameter adjustment is needed if the value is below this. This represents the threshold value for the critical point in resource competition. ; when When doing so, reduce the priority of low-priority tasks according to the above formula. High-priority tasks Keep constant.

6. A novel integrated control system for anti-power fluctuation and power quality according to claim 5, characterized in that, In the preset dynamic resource allocation sub-algorithm invoked by the control module, the hardware resource capacity allocation model is as follows: ,in: To be assigned to the Hardware capacity for each task; This refers to the rated total capacity of the hardware resource modules; For the first The revised priority weights for each task; For the first The urgency coefficient of each task is determined by the characteristic values ​​of parameter changes in precise operational data. Decide: hour , hour , hour ; The total number of tasks that currently require resource allocation; The switching frequency allocation model is ,in: To be assigned to the The switching frequency of each task; This refers to the rated maximum switching frequency of the hardware resource module. For the first Response time requirement coefficient for each task: Power sloshing mitigation Rapid response is required; three-phase imbalance management and harmonic compensation are needed. Reactive power compensation and load voltage regulation .

7. A novel integrated control system for anti-power fluctuation and power quality according to claim 6, characterized in that, After generating resource allocation instructions, the control module uses an instruction feasibility verification formula. ,in: For the first Minimum capacity requirement for each task; For the first Minimum switching frequency requirement for each task; , To be assigned to the The capacity and switching frequency of each task; when At that time, the instruction verification submodule triggers a secondary resource allocation: prioritizing and reducing the priority of low-priority tasks. and , This is a low-priority task, added to In the task, until all tasks are satisfied .

8. A novel integrated control system for anti-power fluctuation and power quality as described in claim 7, characterized in that... The hardware resource module includes two parallel three-phase full-bridge inverters and a switching frequency regulation unit. The anti-power fluctuation circuit includes a supercapacitor energy storage unit, a bidirectional DC-DC converter module, a DC bus voltage monitoring module, and an overcurrent protection module. After receiving the power fluctuation mitigation capacity and switching frequency allocated by the hardware resource module, the bidirectional DC-DC converter module first obtains the current DC bus voltage value through the DC bus voltage monitoring module and compares it with the bus stable voltage required to resist power fluctuation. When the grid voltage drops to ≥5%, the bidirectional DC-DC converter module adjusts the energy release rate according to the allocated capacity, and controls the internal IGBTs to turn on and off according to the allocated switching frequency, converting the electrical energy of the supercapacitor energy storage unit into stable DC power to maintain the stability of the DC bus voltage. The overcurrent protection module monitors the output current of the bidirectional DC-DC converter module in real time. When the current exceeds the rated current, the energy output circuit is immediately cut off.

9. A novel integrated control system for anti-power fluctuation and power quality according to claim 8, characterized in that, The power quality integrated module includes a voltage adjustment submodule, a harmonic compensation submodule, a reactive power compensation submodule, and a three-phase imbalance mitigation submodule. Voltage regulation submodule: includes an autotransformer and a thyristor control module. After receiving the capacity allocated by the hardware resource module, the thyristor control module adjusts the conduction angle according to the capacity to control the output voltage of the autotransformer and ensure the stability of the load voltage. Harmonic compensation submodule: including active power filter and LC passive filter branch. After receiving the capacity and switching frequency allocated by the hardware resource module, the APF detects the harmonic content of the power grid in real time according to the allocated switching frequency, and outputs compensation current according to the allocated capacity. Combined with the LC passive filter branch, it specifically suppresses the 3rd and 5th high-order harmonics, so that the total harmonic distortion rate is reduced to below 5%, avoiding overheating and damage to the motor windings caused by harmonics. Reactive power compensation submodule: includes group switching capacitor banks and reactive power detection module. After receiving the capacity allocated by the hardware resource module, it controls the number of group switching capacitor banks according to the capacity requirements to realize reactive power compensation and improve the power factor from 0.8 to above 0.

95. The three-phase imbalance management submodule includes a three-phase series reactor and a current balance monitoring module. After receiving the capacity allocated by the hardware resource module, it adjusts the inductance value of the three-phase series reactor according to the capacity size to balance the three-phase current and reduce the three-phase imbalance to a preset range. This prevents the unbalanced current from causing local overheating of the distribution transformer and extends the service life of the transformer.

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