Intelligent control method, system and equipment for static var generator module

Abstract

The invention discloses an intelligent control method, system and device for a static var generator module, and the method comprises the steps: writing system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters, so as to complete the initialization and configuration of the parameters; when voltage sags, three-phase signals of a power grid are collected and processed, reactive power is calculated through coordinate transformation, a sliding mode surface is constructed, a gain coefficient is updated, and a control law is output for voltage sag response; when the modules run in parallel, establishing a state space model based on a measured value of a voltage and current sensor, setting a coupling weight coefficient, constructing an optimization objective function, and obtaining a control sequence through iterative calculation; and monitoring key parameters of the system, blocking fault module signals and redistributing power when the key parameters exceed a preset threshold value, and recovering system operation after recording fault information. Through the scheme of the invention, the stability of the system under large disturbance can be ensured, and the multi-module system can achieve power balanced distribution.

Description

Technical Field

[0001] The present application relates to the field of reactive power compensation, and in particular to an intelligent control method, system and device for a static VAR generator module. Background Art

[0002] With the rapid development of power electronics technology and smart grids, power quality issues are receiving increasing attention. Static Var Generator (SVG), as a new generation of dynamic reactive power compensation device, plays a key role in power quality management, voltage regulation and system stability improvement. SVG uses a voltage source converter composed of fully controlled power devices. Through advanced control strategies, it can quickly and continuously adjust reactive power output to achieve dynamic reactive power compensation. Compared with traditional reactive power compensation devices such as fixed capacitor banks and controllable reactors (TCRs), SVG has significant advantages such as fast response speed, continuous compensation characteristics and low harmonic content.

[0003] However, the existing SVG control system has obvious deficiencies when facing grid disturbances. The primary problem is that it has poor adaptability to grid voltage sag conditions. When designing traditional control algorithms, the compensation characteristics under steady-state operating conditions are mainly considered, and the dynamic response characteristics of the system under transient conditions such as voltage sag are not optimized enough. When the grid voltage sags, the reactive power calculation method based on the conventional Park transformation will produce significant errors due to the sudden change of the voltage fundamental component. This causes the SVG compensation response to be significantly delayed, and it is unable to provide the required reactive support in time. In severe cases, it may even trigger protection and disconnect from the grid. Another prominent problem is the coordinated control problem when multiple power modules are operated in parallel. In large-capacity SVG systems, multiple power modules are usually operated in parallel to improve system capacity and reliability. However, due to factors such as manufacturing process and device characteristics, there is inconsistency in the DC side voltage of each power module. The traditional control method does not fully consider the circulation effect between modules when designing, resulting in unbalanced load distribution between power modules. This imbalance phenomenon not only increases the operating loss of the system, but also may cause overstress problems of power devices.

[0004] Therefore, a technical solution is urgently needed to ensure the stability of the system under large disturbances and achieve balanced power distribution in multi-module systems. Summary of the invention

[0005] In order to solve the deficiencies of the prior art, the embodiments of the present application provide an intelligent control method, system and device for a static VAR generator module. The present application solves the technical problems of the prior art such as untimely compensation and uneven load distribution between modules.

[0006] In the first aspect, an embodiment of the present application provides an intelligent control method for a static VAR generator module, including: writing system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters to complete parameter initialization and configuration; when the voltage sags, collecting and processing the three-phase signal of the power grid, calculating the reactive power through coordinate transformation, constructing the sliding mode surface and updating the gain coefficient, and responding to the voltage sag with the output control law; when the modules are operated in parallel, establishing a state space model based on the measurement values ​​of the voltage and current sensors, setting the coupling weight coefficients, constructing the optimization objective function and obtaining the control sequence through iterative calculation; monitoring the key parameters of the system, blocking the fault module signal and redistributing the power when the preset threshold is exceeded, and restoring the system operation after recording the fault information.

[0007] In a possible implementation, system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters are written to complete parameter initialization and configuration, including: obtaining the system rated voltage parameters, system rated capacity parameters, sampling frequency parameters and carrier frequency parameters, and writing the parameters into the controller storage unit; writing the sliding mode control normal constant, nonlinear factor and adaptive adjustment coefficient into the controller storage unit for constructing the sliding surface equation; configuring the system equivalent parameters and prediction step size for iterative calculation of the state prediction equation; and weighting the state tracking error term, control quantity constraint term and control increment constraint term according to the weight matrix parameters.

[0008] In a possible implementation, when the voltage sags, the three-phase signals of the power grid are collected and processed, the reactive power is calculated by coordinate transformation, the sliding surface is constructed and the gain coefficient is updated, and the voltage sag response is performed by outputting a control law, including: when the voltage sags, the three-phase voltage and current signals of the power grid are collected, and the signals are subjected to analog-to-digital conversion and low-pass filtering; the three-phase voltage and current are converted to the target coordinate system by coordinate transformation, and the instantaneous reactive power is calculated; the compensation current error is obtained by measuring the difference between the load current and the reference current calculated based on the instantaneous reactive power theory; an improved sliding surface is constructed based on the compensation current error, and the adaptive gain coefficient is updated in real time; the control law is calculated according to the adaptive gain coefficient to complete the voltage sag rapid response control.

[0009] In a possible implementation, when the modules are operated in parallel, a state space model is established based on the measurement values ​​of voltage and current sensors, coupling weight coefficients are set, an optimization objective function is constructed, and a control sequence is obtained through iterative calculation, including: when the modules are operated in parallel, a voltage sensor is used to measure the DC side voltage, a Hall current sensor is used to measure the AC side current, and a power module state space model is established; the physical connection relationship between the modules is measured to determine the topological structure, and the coupling weight coefficients between the modules are set; a distributed optimization objective function including a state tracking error term, a control quantity constraint term, and a control increment constraint term is constructed; and the optimization objective function is iteratively calculated using a fast gradient method until the convergence conditions are met, and an optimal control sequence is output.

[0010] In a possible implementation, key system parameters are monitored, and when a preset threshold is exceeded, the fault module signal is blocked and power is redistributed, and the system operation is restored after the fault information is recorded, including: real-time monitoring of DC side voltage, AC side current, device temperature and zero-sequence current to determine whether they exceed the preset protection threshold; when any parameter is detected to exceed the preset protection threshold, the pulse signal of the fault module is blocked and the corresponding bypass switch is closed; the power allocation plan of the remaining power modules is recalculated, and the fault information is sent to the target system; the fault type, fault time and related parameters are recorded, the fault processing is completed and the system operation is restored.

[0011] In a possible implementation, an improved sliding surface is constructed based on the compensation current error, including: S(t)=2.357i1(t)+∫[k1(t)sgn(i1(t))+k2(t)|i1(t)| 0.783 ]dt, where S(t) represents, i1(t) represents the compensation current error, k1(t) and k2(t) represent the adaptive gain coefficients, and sgn() represents the sign function.

[0012] In a possible implementation, establishing a power module state space model includes: Among them, x i (k+1) represents the state vector of the i module at time k+1, x i (k) represents the state vector of the i-th module at time k, A represents the system state matrix, B represents the control input matrix, and u i (k) represents the control input of the i-th module at time k, represents the set of modules adjacent to module i, w ij represents the coupling weight between module i and module j, x j (k) represents the state vector of the jth module at time k.

[0013] In a possible implementation, constructing a distributed optimization objective function including a state tracking error term, a control quantity constraint term, and a control increment constraint term includes: Among them, J i represents the optimization objective function of the i-th power module, N p represents the prediction time domain length, p represents the step number, x i (k+p|k) represents the state prediction of the i-th module at time k for the p-th step in the future, x ref Indicates the state reference value, u i (k+p|k) represents the control input prediction of the i-th module at time k for the p-th step in the future, Δu i (k+p|k) represents the control input increment, and k represents the current discrete time step.

[0014] In the second aspect, an embodiment of the present application also provides an intelligent control system for a static VAR generator module, which is used to implement the intelligent control method as described in any of the above embodiments, and the system includes: a configuration unit, a control unit and a monitoring unit; wherein the configuration unit is used to write system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters to complete parameter initialization and configuration; the control unit is used to collect and process the three-phase signal of the power grid when the voltage sags, calculate the reactive power through coordinate transformation, construct the sliding mode surface and update the gain coefficient, and respond to the voltage sag with the output control law; when the modules are operated in parallel, a state space model is established based on the measurement values ​​of the voltage and current sensors, the coupling weight coefficients are set, the optimization objective function is constructed and the control sequence is obtained through iterative calculation; the monitoring unit is used to monitor the key parameters of the system, block the fault module signal and redistribute the power when the preset threshold is exceeded, and restore the system operation after recording the fault information.

[0015] In the third aspect, an embodiment of the present application also provides an intelligent control device for a static VAR generator module, comprising: a processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute the method described in any one of the above embodiments.

[0016] Compared with the prior art, this application has achieved the following beneficial effects:

[0017] The present invention discloses an intelligent control method, system and device for a static VAR generator module. By introducing adaptive gain in the sliding surface, the control law can be quickly calculated and output under voltage sag conditions, which significantly improves the dynamic response speed of the system and reduces the compensation lag time. The real-time update mechanism of the adaptive gain coefficient makes the system more robust in the face of voltage mutations and effectively suppresses the steady-state error. The distributed predictive control strategy fully considers the coupling relationship between modules, and realizes the balanced distribution of power between modules by constructing a state space model and a distributed optimization objective function. The predictive control combined with the fast gradient method can optimize the control sequence of each module while ensuring the stability of the system, and improve the overall compensation performance of the system. The establishment of a fault diagnosis and processing mechanism can quickly cut off the PWM signal of the faulty module when a fault is detected, and start the protection program to prevent the fault from spreading, thereby improving the reliability of the system. The dynamic redundancy design allows the system to automatically adjust the power distribution of the remaining modules when any module fails, ensuring that the system continues to operate stably and improving the fault tolerance of the system. Through collaborative control and precise modeling between modules, the system can more effectively suppress high-order harmonics, reduce the total harmonic distortion rate, and improve the power quality of the power grid. The control system adopts a hierarchical structure design, making the system easy to expand and maintain. Through precise control algorithms and optimized power distribution schemes, the system can reduce operating losses and improve system efficiency while ensuring compensation effects. The distributed control strategy enables the system to maintain good stability and consistency when multiple modules are running in parallel, avoiding the problems of uneven circulation and load distribution between modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A schematic flow chart of an intelligent control method for a static VAR generator module provided in an embodiment of the present application;

[0020] Figure 2 A schematic flow chart of a method for responding to a voltage sag of a static VAR generator module provided in an embodiment of the present application;

[0021] Figure 3 A schematic flow chart of a method for parallel operation of static VAR generator modules provided in an embodiment of the present application;

[0022] Figure 4 A schematic block diagram of an intelligent control system for a static VAR generator module provided in an embodiment of the present application;

[0023] Figure 5 A schematic curve diagram of the parallel performance of a static VAR generator provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0025] Those skilled in the art can understand that the terms "first", "second" and the like in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor represent the necessary logical order between them. It should also be understood that in the embodiments of the present disclosure, "multiple" can refer to two or more, and "at least one" can refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present disclosure, in the absence of explicit limitation or contrary revelation given in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present disclosure is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are an "or" relationship. It should also be understood that the description of each embodiment in the present disclosure emphasizes the differences between the embodiments, and the same or similar parts can refer to each other. For the sake of brevity, they will not be repeated one by one.

[0026] At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. The techniques, methods and devices known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and devices should be considered part of the specification. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] Figure 1 A schematic flow chart of an intelligent control method for a static VAR generator module provided in an embodiment of the present application. It should be noted that a static VAR generator (SVG) is a novel dynamic reactive compensation device, mainly composed of a power unit, a control system, and connection equipment. The power unit used in the embodiment of the present application is a three-phase H-bridge series structure, and each phase contains 4 IGBT power modules (the model is ABB's 5SNA1600N170100, with a rated voltage of 1.7kV and a rated current of 1600A) and a corresponding drive circuit. The control system adopts a dual-core architecture of DSP+FPGA, in which the DSP uses TI's TMS320F28377D with a main frequency of 200MHz, and the FPGA uses Xilinx's XC7A50T-2FGG484.

[0029] It should be understood that in the prior art, SVG achieves reactive power compensation by controlling the phase and amplitude of the AC side voltage of the bridge circuit. In specific implementation, the instantaneous reactive power theory is mainly used to calculate the reference current, and then the PWM wave is generated through current tracking control to drive the power device switch. This control method has two main technical problems:

[0030] Under the condition of grid voltage sag, the traditional control algorithm only considers the compensation characteristics under steady-state operation conditions, and does not optimize the dynamic response characteristics at the moment of voltage sag, which leads to the SVG's slow response when voltage sag occurs, or even disconnects from the grid and stops operating. Specifically, when the controller calculates the compensation current, the grid fundamental component suddenly changes due to the voltage sag, resulting in a large error in the reactive power calculation based on the conventional Park transformation, and the compensation is not timely.

[0031] When multiple power modules are connected in parallel, the DC side voltage of each module is inconsistent, and the traditional control does not fully consider the impact of the circulation between modules, resulting in uneven load distribution between power modules. Specifically, the DC side capacitor voltage of each half-bridge unit fluctuates greatly, and there is circulation loss between modules, which affects the overall compensation effect of the system.

[0032] In order to solve the above technical problems, the present application discloses an intelligent control method for a static VAR generator, such as Figure 1As shown, in step S101, the system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters are written to complete parameter initialization and configuration. This includes: obtaining the system rated voltage parameters, system rated capacity parameters, sampling frequency parameters and carrier frequency parameters, and writing the parameters into the controller storage unit; writing the sliding mode control normal constant, nonlinear factor and adaptive adjustment coefficient into the controller storage unit for constructing the sliding surface equation; configuring the system equivalent parameters and prediction step length for iterative calculation of the state prediction equation; and weighting the state tracking error term, control quantity constraint term and control increment constraint term according to the weight matrix parameters.

[0033] According to the above-mentioned embodiments of the present application, by initializing and configuring parameters, it is ensured that the parameters of each module are consistent in the initial state, and accurate basic data is provided for the subsequent control algorithm, thereby improving the stability and reliability of the system. The sliding mode control parameters are introduced to construct a sliding surface equation that is more suitable for transient conditions, which can improve the dynamic response speed and robustness of the system under transient conditions such as voltage sag. By configuring the system equivalent parameters and prediction step size, and iteratively calculating the state prediction equation based on these parameters, the system state can be predicted more accurately and the control effect can be optimized. The control target is weighted and configured through the weight matrix parameters to ensure that each module remains coordinated and consistent during the control process, thereby improving the overall performance and stability of the system.

[0034] In one embodiment, the key system parameters are configured first. The basic operating parameters of the system include: the grid rated voltage of 10kV, which is used to determine the system insulation level and protection setting value; the system rated capacity of 10Mvar, which determines the selection of IGBT modules and the design of the heat dissipation system; the sampling frequency of 10kHz: to ensure the sampling theorem requirements, used for AD converter configuration; the PWM carrier frequency of 2.5kHz: used for the clock division setting of the PWM module in the FPGA.

[0035] As for the core parameter configuration in the control algorithm, in the sliding mode control parameters: the positive constant c = 2.357, used to construct the sliding surface S(t) = 2.357 × i1(t) + ∫[…]dt; the nonlinear factor α = 0.783, applied to the nonlinear term |i1(t)| 0.783 Calculation; Adaptive adjustment coefficients γ1 = 0.0467 and γ2 = 0.0325, used for gain update equations k1(t) = 0.0467 × | S (t) | × | i1 (t) | and k2(t) = 0.0325 × | S (t) | × | i1 (t) | 0.783 ; System equivalent parameters R = 0.235Ω and L = 3.27mH, used for control law calculation

[0036] Prediction control parameters: prediction step p = 3, determine the state prediction equation x i The number of iterations (k+1…k+3); the weight moment Q=diag(1.5, 1.2, 1.0) is used for the state tracking error term ||x i (k+p|k)-x ref || 2 The weighting of R = 0.8I is used for the control quantity constraint term ||u i (k+p|k)|| 2 The weight of S = 0.6I is used to control the incremental constraint term ||Δu i (k+p|k)|| 2 These weight matrices directly determine the calculation results of the optimization objective function.

[0037] At step S102, when the voltage drops temporarily (e.g. Figure 2 As shown in the figure, the three-phase signals of the power grid are collected and processed, the reactive power is calculated through coordinate transformation, the sliding mode surface is constructed and the gain coefficient is updated, and the voltage sag response is performed with the output control law.

[0038] These include: when the voltage sags, collecting the three-phase voltage and current signals of the power grid, and performing analog-to-digital conversion and low-pass filtering on the signals; converting the three-phase voltage and current to the target coordinate system through coordinate transformation, and calculating the instantaneous reactive power; obtaining the compensation current error by measuring the difference between the load current and the reference current calculated based on the instantaneous reactive power theory; constructing an improved sliding surface based on the compensation current error, and updating the adaptive gain coefficient in real time; calculating the control law according to the adaptive gain coefficient to complete the rapid response control of voltage sag.

[0039] At step S103, when the modules are operated in parallel (such as Figure 3 As shown in the figure, a state space model is established based on the measured values ​​of voltage and current sensors, the coupling weight coefficient is set, the optimization objective function is constructed, and the control sequence is obtained through iterative calculation.

[0040] These include: when the modules are operated in parallel, a voltage sensor is used to measure the DC side voltage, a Hall current sensor is used to measure the AC side current, and a power module state space model is established; the physical connection relationship between the modules is measured to determine the topology structure, and the coupling weight coefficient between the modules is set; a distributed optimization objective function including state tracking error terms, control quantity constraint terms and control increment constraint terms is constructed; the fast gradient method is used to iteratively calculate the optimization objective function until the convergence conditions are met, and the optimal control sequence is output.

[0041] At step S104, the key parameters of the system are monitored. When the preset threshold is exceeded, the fault module signal is blocked and the power is redistributed. The system operation is restored after recording the fault information. This includes: real-time monitoring of the DC side voltage, AC side current, device temperature and zero-sequence current to determine whether it exceeds the preset protection threshold; when any parameter is detected to exceed the preset protection threshold, the pulse signal of the fault module is blocked and the corresponding bypass switch is closed; the power allocation plan of the remaining power modules is recalculated, and the fault information is sent to the target system; the fault type, fault time and related parameters are recorded, the fault processing is completed and the system operation is restored.

[0042] In one embodiment, the following protection functions are required to ensure the safe operation of the system. Overvoltage protection: When the DC side voltage exceeds 1.2 times the rated value, the protection is triggered. Overcurrent protection: When the AC side current exceeds 1.5 times the rated value, the protection is triggered. Temperature protection: When the IGBT junction temperature exceeds 125°C, the protection is triggered. Ground protection: Zero-sequence current protection is adopted, and the operating current is set to 5A. When a power module fault is detected, the control system processes it according to the following steps: immediately block the PWM signal of the faulty module; close the corresponding bypass switch; reallocate the power of the remaining modules; and report the fault information to the superior control system.

[0043] Figure 2 A schematic flow chart of a method for responding to a voltage sag in a static VAR generator module provided in an embodiment of the present application.

[0044] like Figure 2 As shown, at step S201, when the voltage drops temporarily, the three-phase voltage and current signals of the power grid are collected, and the signals are subjected to analog-to-digital conversion and low-pass filtering. For example, the three-phase voltage and current signals of the power grid can be measured by PT (model JDZ10-10) and CT (model LZZBJ9-10 / 5), and the sampled values ​​are converted by a 16-bit AD converter (model ADS8568) and sent to the DSP. The sampled data is processed by a digital low-pass filter, and the cut-off frequency is set to 2kHz.

[0045] In step S202, the three-phase voltage and current are converted to the target coordinate system through coordinate transformation, and the instantaneous reactive power is calculated.

[0046] In step S203, a compensation current error is obtained by measuring the difference between the load current and a reference current calculated based on instantaneous reactive power theory.

[0047] At step S204, an improved sliding surface is constructed based on the compensated current error, and the adaptive gain coefficient is updated in real time. The improved sliding surface is constructed based on the compensated current error, including: S(t) = 2.357i1(t) + ∫ [k1(t)sgn(i1(t)) + k2(t) | i1(t) |0.783 ]dt, where S(t) represents, i1(t) represents the compensation current error, k1(t) and k2(t) represent the adaptive gain coefficients, and sgn() represents the sign function.

[0048] Specifically, after calculating the compensation current error i1(t), it is obtained by the difference between the load current measured in real time and the reference current calculated based on the instantaneous reactive power theory. The calculation process here is as follows: First, the three-phase voltage and current are converted to the αβ coordinate system by Clark transformation: Then calculate the instantaneous reactive power: q = v α i β -v β i α Based on this, an improved sliding surface S(t) is constructed. In the DSP, the floating point unit is used to perform the following calculation: S(t) = 2.357i1(t) + ∫[k1(t)sgn(i1(t)) + k2(t)|i1(t)| 0.783 ]dt, where the adaptive gain coefficients k1(t) and k2(t) are updated in real time by the following formula: k1(t) = 0.0467 × |S(t)| × |i1(t)|, k2(t) = 0.0325 × |S(t)| × |i1(t)| 0.783 .

[0049] At step S205, the control law is calculated according to the adaptive gain coefficient to complete the voltage sag fast response control.

[0050] Figure 3 A flowchart of a method for parallel operation of static VAR generator modules provided in an embodiment of the present application. For the scenario where multiple power modules are operated in parallel, a distributed predictive control strategy is used to achieve coordination between modules. The specific implementation steps are as follows:

[0051] like Figure 3 As shown, at step S301, when the modules are operated in parallel, a voltage sensor is used to measure the DC side voltage, and a Hall current sensor is used to measure the AC side current, and a power module state space model is established. The establishment of the power module state space model includes: Among them, x i (k+1) represents the state vector of the i module at time k+1, x i (k) represents the state vector of the i-th module at time k, A represents the system state matrix, B represents the control input matrix, and u i (k) represents the control input of the i-th module at time k, represents the set of modules adjacent to module i, w ij represents the coupling weight between module i and module j, xj (k) represents the state vector of the jth module at time k.

[0052] Specifically, firstly, the state space model of each power module is established. Taking a single power module as an example, the state variables include the DC side capacitor voltage v dc and the AC side current i abc . Model parameter measurement method: Use a high-precision voltage sensor (model LV25-P) to measure the DC side voltage, and use a Hall current sensor (model LA55-P) to measure the AC side current. Then, the inter-module coupling prediction model is established based on the measurement data. For the i-th power module, its discrete state equation is:

[0053] In step S302, the physical connection relationship between modules is measured to determine the topology structure, and the coupling weight coefficient between modules is set. First, the topology structure is determined by measuring the physical connection relationship between modules, and then the initial weight is set based on the topology structure (0.235 between adjacent modules and 0 between non-adjacent modules), and finally optimized and adjusted through actual operation data.

[0054] At step S303, a distributed optimization objective function including a state tracking error term, a control amount constraint term and a control increment constraint term is constructed, which includes: Among them, J i represents the optimization objective function of the i-th power module, N p represents the prediction time domain length, p represents the step number, x i (k+p|k) represents the state prediction of the i-th module at time k for the p-th step in the future, x ref Indicates the state reference value, u i (k+p|k) represents the control input prediction of the i-th module at time k for the p-th step in the future, Δu i (k+p|k) represents the control input increment, and k represents the current discrete time step.

[0055] In step S304, the fast gradient method is used to iteratively calculate the optimization objective function until the convergence condition is met, and the optimal control sequence is output. The improved fast gradient method can be used to solve the optimization problem. The specific steps are: Initialize the control sequence u i (k); Calculate the objective function value J i and gradient Use the Armijo criterion to determine the step size α; update the control sequence: Determine whether the convergence condition ||u is met i (k+1)-u i (k)||<0.001; if not satisfied, return to calculate the objective function value J i and gradient Otherwise, the optimal control sequence is output.

[0056] Figure 4 A schematic block diagram of an intelligent control system for a static VAR generator module provided in an embodiment of the present application. It should be understood that the system shown in the figure is exemplary and non-restrictive. This means that the system architecture involved is not limited to a specific form or design, but is presented as an example. In other words, the architecture shown in the figure can be regarded as a way of expression to clearly describe related concepts and relationships, and does not exclude other forms of architecture. Therefore, when interpreting the architecture in the picture, it should be understood that the model has flexibility and diversity, and its purpose is to provide an exemplary description rather than a restrictive provision on a specific form.

[0057] The present application discloses an intelligent control system for a static VAR generator module, which is used to implement the intelligent control method described in the above embodiment. The system includes: a configuration unit 401, a control unit 402 and a monitoring unit 403; wherein the configuration unit 401 is used to write system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters to complete parameter initialization and configuration; the control unit 402 is used to collect and process the three-phase signal of the power grid when the voltage sags, calculate the reactive power through coordinate transformation, construct the sliding mode surface and update the gain coefficient, and respond to the voltage sag with the output control law; when the modules are operated in parallel, a state space model is established based on the measured values ​​of the voltage and current sensors, the coupling weight coefficient is set, the optimization objective function is constructed and the control sequence is obtained through iterative calculation; the monitoring unit 403 is used to monitor the key parameters of the system, block the fault module signal and redistribute the power when the preset threshold is exceeded, and restore the system operation after recording the fault information.

[0058] The specific implementation process of the voltage sag fast response control system starts with the acquisition of power grid signals. In the voltage and current sampling link, the sampled voltage signal is transformed into 100V through PT, and the sampled current signal is transformed into 5A through CT. The sampled signal is pre-processed through the RC filter circuit, and the cutoff frequency of the RC filter circuit is set to 5kHz to ensure the quality of the sampled signal. The pre-processed analog signal is sent to the 16-bit AD converter ADS8568 for digital conversion. The sampling clock of the converter is provided by the GPIO output port of the DSP with a 10MHz clock signal to achieve synchronous sampling.

[0059] After being processed by DSP, the digital signal is sent to FPGA through a high-speed serial communication interface. FPGA uses Xilinx's XC7A50T-2FGG484 chip, which integrates a dedicated PWM module and digital filter. After receiving the digital signal, FPGA first processes it through a digital low-pass filter. The filter uses a 48-order FIR structure with a cutoff frequency of 2kHz. The filter coefficients are designed using MATLAB's FDA tool. The filtered signal is sent to the Clark transformation module to complete the conversion from a three-phase stationary coordinate system to a two-phase stationary coordinate system.

[0060] In the two-phase stationary coordinate system, the instantaneous reactive power is calculated. The calculated instantaneous reactive power value is compared with the given value to obtain an error signal. The error signal is used as the input of the improved adaptive damping sliding mode controller. The core of the sliding mode controller is to construct a suitable sliding surface and control law. The construction of the sliding surface adopts the form of integral terms, and the steady-state error of the system can be effectively suppressed through the integral action. The design of the control law introduces nonlinear factors and adaptive gains to improve the dynamic response performance of the system.

[0061] Inside the FPGA, based on the calculated control quantity, the PWM drive signal is generated by the space vector PWM algorithm. The PWM signal is generated by symmetrical modulation, and the carrier frequency is set to 2.5kHz. Considering the influence of the dead time, a 2μs dead time delay is added to the rising and falling edges of the PWM signal. The PWM signal is transmitted to the IGBT drive circuit via optical fiber. The drive circuit adopts a dual-channel isolated power supply method, and the drive power supply uses a DC / DC module to provide +15V / -8V drive voltage.

[0062] The driving and protection of the IGBT power module is the key to the stable operation of the system. The driving circuit adopts a dual-path interlocking form, which is implemented by a dedicated IGBT driver chip. The driver chip integrates protection functions such as short-circuit protection, overcurrent protection and undervoltage lockout. Short-circuit protection uses the method of detecting VCE voltage, the protection threshold is set to 5.5V, and the response time is less than 1μs. Overcurrent protection is based on the sampling value of the Hall current sensor, and the protection threshold is set to 1.5 times the rated current. The undervoltage lockout voltage is set to 12V to ensure that the IGBT works in a safe area.

[0063] In order to improve the reliability of the system, a complete fault diagnosis and processing mechanism is also added to the control system. Fault diagnosis mainly includes IGBT failure detection, DC bus voltage abnormality detection and system overtemperature detection. IGBT failure detection is based on VCE voltage and gate feedback signal. When an IGBT short circuit or open circuit fault is detected, the control system cuts off the PWM signal within 4μs and starts the protection program. The DC bus voltage abnormality detection uses a voltage sensor for real-time monitoring. When the voltage exceeds 1.2 times or is less than 0.8 times the rated value, the protection is triggered. The system overtemperature detection measures the IGBT junction temperature through a thermistor, and the protection is triggered when the temperature exceeds 125℃.

[0064] In terms of heat dissipation system design, a combination of air cooling and water cooling is adopted. The coolant of the water cooling system is deionized water with a flow rate of 20L / min, and the water inlet temperature is controlled below 25°C. The air cooling system uses a variable speed fan, which automatically adjusts the speed according to the IGBT junction temperature. Through this composite heat dissipation method, the junction temperature of the IGBT can be controlled below 80°C, ensuring long-term stable operation of the system.

[0065] The overall coordinated control of the system adopts a hierarchical structure. The upper layer is the system-level controller, which is responsible for power distribution and operation mode switching; the lower layer is the module-level controller, which executes specific control algorithms. The two layers of controllers communicate through the CAN bus, and the communication cycle is 10ms. The system-level controller calculates the optimal power distribution plan in real time according to the load characteristics and system operation status, and sends instructions to each module. After receiving the instructions, the module-level controller executes the corresponding control strategy according to the local measurement data, and feeds back the operation status to the system-level controller. The system-level controller is responsible for power distribution and operation mode switching, and the module-level controller executes specific control algorithms. The two realize data interaction through the communication bus, which improves the flexibility and scalability of the system.

[0066] In practical applications, the system startup process needs to be carried out in strict sequence. First, power the control system and wait for the DSP and FPGA to complete initialization; then power the drive circuit and check whether each drive signal is normal; finally, power the power unit and gradually increase the voltage to the rated voltage. The entire startup process takes about 30 seconds, and the system enters normal operation after the startup is completed. The shutdown process requires first reducing the power output, and then gradually cutting off the power in the opposite order. This strict start-stop program design can effectively prevent voltage and current shocks during the start-stop process and increase the service life of the system.

[0067] Figure 5A schematic curve diagram of the parallel performance of a static VAR generator provided in an embodiment of the present application. In-depth analysis and performance verification are carried out from the perspective of practical application for the parallel operation characteristics of the static VAR generator. In the actual operation process, a distributed predictive control model is established to comprehensively test and analyze the system response characteristics. The test process first adopts a single-module operation mode, collects DC side voltage data through a high-precision voltage sensor LV25-P, and uses a Hall current sensor LA55-P to obtain AC side current data to establish a benchmark performance curve. It then switches to a multi-module parallel operation mode, and compares and analyzes the dynamic response characteristics under the two operating modes.

[0068] During the system startup phase, the inter-module communication link test is first performed. The communication network is built through an industrial Ethernet switch, and each power module realizes data interaction through the TCP / IP protocol. The communication cycle is set to 100μs. After the communication test is completed, the parameter self-tuning phase begins. In this phase, the standard test signal is injected, the system response data is collected, and the equivalent parameters of the system are identified using the least squares method. According to the identification results, the controller parameters are automatically calculated and updated, including the normal constant c value and the nonlinear factor α value in the sliding mode control, as well as the specific values ​​of the weight matrices Q, R, and S in the predictive control.

[0069] In the steady-state operation stage, the system shows significant performance differences. When the single module is running, the system responds quickly, but there is a certain overshoot. It can be seen from the curve that there is an obvious response inflection point at 10ms, and the overshoot reaches 15% of the rated value. By introducing the improved adaptive damping sliding mode control, the system gradually stabilizes after 20ms, and the steady-state error is controlled within 3%. In the multi-module parallel operation mode, thanks to the coordination of the distributed predictive control strategy, the system presents a smoother dynamic response characteristic. From the curve performance, although the response speed is slightly lower than that of the single module operation, the overshoot is significantly reduced, only 7% of the rated value, and the steady-state error is further reduced to within 1.5%.

[0070] In terms of compensation performance, the multi-module parallel operation mode shows unique advantages. Through dynamic power allocation between modules, the system can respond to load fluctuations more flexibly. When a sudden load change is detected, the distributed controller can quickly calculate the optimal power allocation plan within adjacent sampling cycles. Specifically for the control process, the system first obtains the instantaneous reactive power value in the αβ coordinate system through Clark transformation based on the real-time collected three-phase voltage and current signals of the power grid. Subsequently, the predictive controller predicts the future state trajectory with a span of 3 sampling cycles. On this basis, the coordinated control of the output power of each module is achieved by solving the distributed optimization objective function.

[0071] In order to improve system reliability, dynamic redundancy design is also introduced in the multi-module parallel solution. When any power module fails, the system can automatically adjust the power distribution of the remaining modules without affecting the overall operation. This process is specifically manifested as follows: when it is detected that the IGBT junction temperature exceeds 125°C or the module output current exceeds 1.5 times the rated value, the control system immediately starts the protection program, blocks the PWM signal of the faulty module and closes the bypass switch. At the same time, the distributed predictive controller automatically recalculates the coupling weight coefficient w_ij and updates the state space model of each power module to ensure that the system quickly transitions to a new steady-state operating point.

[0072] In terms of harmonic control, the multi-module parallel operation scheme also shows significant advantages. Through collaborative control between modules, the system can more effectively suppress high-order harmonics. Test data shows that under rated conditions, the system has a significant compensation effect on 2-13 harmonics, and the total harmonic distortion rate is reduced from the original 8.2% to 2.1%. This is mainly due to the superiority of the distributed predictive control strategy in dealing with nonlinear loads. Through precise modeling and dynamic optimization, accurate compensation for each harmonic is achieved.

[0073] Furthermore, an embodiment of the present application also provides an intelligent control device for a static VAR generator module, comprising: a processor, a memory, and a system bus; the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute any of the above methods.

[0074] Furthermore, an embodiment of the present application also provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute any of the above methods.

[0075] It can be known from the description of the above implementation mode that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., including several instructions for enabling a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0076] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0077] It should also be noted that, in the embodiments of the present application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements that are not clearly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the statement "comprise one..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0078] The above description of the disclosed embodiments enables professionals and technicians in the field to implement or use the present application. Various modifications to these embodiments will be apparent to professionals and technicians in the field, and the general principles defined in the embodiments of the present application can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown in the embodiments of the present application, but will conform to the widest range consistent with the principles and novel features disclosed in the embodiments of the present application.

Claims

1. An intelligent control method for a static VAR generator module, characterized in that: include: Write the system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters to complete parameter initialization and configuration; When the voltage sags, the three-phase signals of the power grid are collected and processed, the reactive power is calculated through coordinate transformation, the sliding mode surface is constructed and the gain coefficient is updated, and the voltage sag response is performed with the output control law; When the modules are operated in parallel, a state space model is established based on the measured values ​​of the voltage and current sensors, the coupling weight coefficients are set, the optimization objective function is constructed, and the control sequence is obtained through iterative calculation; Monitor key system parameters. When the preset threshold is exceeded, block the fault module signal and redistribute power. Record the fault information and restore system operation.

2. The intelligent control method according to claim 1, characterized in that: in, Write the system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters to complete parameter initialization and configuration, including: Acquire the rated voltage parameters, the rated capacity parameters, the sampling frequency parameters and the carrier frequency parameters of the system, and write the parameters into the controller storage unit; Writing the sliding mode control normal constant, nonlinear factor and adaptive adjustment coefficient into the controller storage unit for constructing the sliding surface equation; Configure system equivalent parameters and prediction step size for iterative calculation of state prediction equation; The state tracking error term, control quantity constraint term and control increment constraint term are weighted and configured according to the weight matrix parameters.

3. The intelligent control method according to claim 1, characterized in that: in, When the voltage sags, the three-phase signals of the power grid are collected and processed, the reactive power is calculated through coordinate transformation, the sliding surface is constructed and the gain coefficient is updated, and the voltage sag response is performed with the output control law, including: When the voltage drops temporarily, the three-phase voltage signal and current signal of the power grid are collected, and the signals are subjected to analog-to-digital conversion and low-pass filtering; The three-phase voltage and current are converted to the target coordinate system through coordinate transformation, and the instantaneous reactive power is calculated; The compensation current error is obtained by measuring the difference between the load current and the reference current calculated based on the instantaneous reactive power theory; An improved sliding surface is constructed based on the compensated current error, and the adaptive gain coefficient is updated in real time; The control law is calculated according to the adaptive gain coefficient to complete the voltage sag fast response control.

4. The intelligent control method according to claim 1, characterized in that: in, When the modules are connected in parallel, a state space model is established based on the voltage and current sensor measurements, the coupling weight coefficient is set, the optimization objective function is constructed, and the control sequence is obtained through iterative calculation, including: When the modules are operated in parallel, a voltage sensor is used to measure the DC side voltage, and a Hall current sensor is used to measure the AC side current, and a power module state space model is established; Measure the physical connection relationship between modules to determine the topology structure and set the coupling weight coefficient between modules; Construct a distributed optimization objective function including state tracking error term, control quantity constraint term and control increment constraint term; The fast gradient method is used to iteratively calculate the optimization objective function until the convergence condition is met and the optimal control sequence is output.

5. The intelligent control method according to claim 1, characterized in that: in, Monitor key system parameters, block fault module signals and redistribute power when they exceed preset thresholds, and restore system operation after recording fault information, including: Real-time monitoring of DC side voltage, AC side current, device temperature and zero-sequence current to determine whether they exceed the preset protection threshold; When any parameter is detected to exceed the preset protection threshold, the pulse signal of the fault module is blocked and the corresponding bypass switch is closed; Recalculate the power allocation plan for the remaining power modules and send the fault information to the target system; Record the fault type, fault time and related parameters, complete fault handling and restore system operation.

6. The intelligent control method according to claim 3, characterized in that: in, An improved sliding surface is constructed based on the compensated current error, including: S(t)=2.357i1(t)+∫[k1(t)sgn(i1(t))+k2(t)|i1(t)| 0.783 ]dt, Among them, S(t) represents, i1(t) represents the compensation current error, k1(t) and k2(t) represent the adaptive gain coefficients, and sgn() represents the sign function.

7. The intelligent control method according to claim 4, characterized in that: in, Establish the power module state space model, including: Among them, x i (k+1) represents the state vector of the i-th module at time k+1, x i (k) represents the state vector of the i-th module at time k, A represents the system state matrix, B represents the control input matrix, and u i (k) represents the control input of the i-th module at time k, represents the set of modules adjacent to module i, w ij represents the coupling weight between module i and module j, x j (k) represents the state vector of the jth module at time k.

8. The intelligent control method according to claim 4, characterized in that: in, Construct a distributed optimization objective function containing state tracking error terms, control quantity constraint terms, and control increment constraint terms, including: Among them, J i represents the optimization objective function of the i-th power module, N p represents the prediction time domain length, p represents the step number, x i (k+p|k) represents the state prediction of the i-th module at time k for the p-th step in the future, x ref Indicates the state reference value, u i (k+p|k) represents the control input prediction of the i-th module at time k for the p-th step in the future, Δu i (k+p|k) represents the control input increment, and k represents the current discrete time step.

9. An intelligent control system for a static VAR generator module, used to implement the intelligent control method according to any one of claims 1 to 8, characterized in that: The system comprises: a configuration unit, a control unit and a monitoring unit; wherein, The configuration unit is used to write system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters to complete parameter initialization and configuration; The control unit is used to collect and process the three-phase signals of the power grid when the voltage sags, calculate the reactive power through coordinate transformation, construct the sliding surface and update the gain coefficient, and respond to the voltage sag with the output control law; when the modules are operated in parallel, a state space model is established based on the measured values ​​of the voltage and current sensors, the coupling weight coefficient is set, the optimization objective function is constructed, and the control sequence is obtained through iterative calculation; The monitoring unit is used to monitor key system parameters. When a preset threshold is exceeded, the fault module signal is blocked and power is redistributed, and the system operation is restored after the fault information is recorded.

10. An intelligent control device for a static VAR generator module, characterized in that: include: A processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; The memory is used to store one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the method according to any one of claims 1 to 8.

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