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

Abstract

The application discloses an intelligent control method, system and equipment of a static reactive generator module, and the method comprises the following steps: writing system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters to complete parameter initialization and configuration; when voltage sag occurs, collecting and processing three-phase signals of a power grid, calculating reactive power through coordinate transformation, constructing a sliding mode surface and updating a gain coefficient to output a control law for voltage sag response; when the module is operated in parallel, establishing a state space model based on voltage and current sensor measurement values, setting a coupling weight coefficient, constructing an optimization objective function and obtaining a control sequence through iterative calculation; monitoring key parameters of the system, blocking a fault module signal and reallocating power when a preset threshold is exceeded, and restoring system operation after recording fault information. Through the scheme of the application, the stability of the system under large disturbance can be ensured, and the power of the multi-module system can be evenly distributed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of reactive power compensation, and particularly relates to an intelligent control method, system and device of a static var generator module. BACKGROUND

[0002] With the rapid development of power electronics technology and smart grid, power quality problems are increasingly valued. As a new generation of dynamic reactive power compensation device, static var generator (SVG) plays a key role in power quality management, voltage regulation and system stability improvement. SVG uses voltage source converter composed of fully controlled power devices, which can quickly and continuously adjust the reactive power output through advanced control strategy, and realize dynamic reactive power compensation. Compared with traditional fixed capacitor bank, controllable reactor (TCR) and other reactive power compensation devices, SVG has obvious 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 power grid disturbance. The primary problem is poor adaptability to power grid voltage sag conditions. The traditional control algorithm mainly considers the compensation characteristics under steady-state operating conditions in the design, and the dynamic response characteristics of the system under transient conditions such as voltage sag are not optimized. When the power grid experiences voltage sag, the voltage fundamental component will change abruptly, and the reactive power calculation method based on conventional Park transformation will produce significant errors. This leads to a significant lag in SVG compensation response, which cannot provide the required reactive power support in time, and in severe cases may even trigger protection and leave the power grid. Another outstanding problem is the coordinated control of multiple power modules in parallel operation. In large-capacity SVG systems, multiple power modules are usually used in parallel operation to improve system capacity and reliability. However, due to factors such as manufacturing process and device characteristic differences, the DC side voltages of each power module are inconsistent. The traditional control method does not fully consider the circulating current effect between modules in the design, resulting in uneven load distribution between power modules. This imbalance not only increases the operating loss of the system, but also may cause over-stress problems of power devices.

[0004] Therefore, there is an urgent need for a technical solution to ensure the stability of the system under large disturbance and to achieve power balanced distribution in a multi-module system. SUMMARY

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

[0006] In a first aspect, the embodiments of the present application provide an intelligent control method of a static reactive generator module, comprising: writing system rated parameters, sliding mode control parameters, system equivalent parameters and weight matrix parameters to complete parameter initialization and configuration; when voltage sag occurs, collecting and processing three-phase grid voltage signals, calculating reactive power through coordinate transformation, constructing a sliding surface and updating gain coefficients to output a control law for voltage sag response; when the module is operated in parallel, establishing a state space model based on voltage and current sensor measurements, setting coupling weight coefficients, constructing an optimization objective function and obtaining a control sequence through iterative calculation; monitoring key system parameters, blocking fault module signals and reallocating power when the parameters exceed a preset threshold, and restoring system operation after recording fault information.

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

[0008] In an implementation manner, when voltage sag occurs, the three-phase grid voltage signals are collected and processed, the reactive power is calculated through coordinate transformation, the sliding surface is constructed and the gain coefficients are updated to output the control law for voltage sag response, which comprises: when voltage sag occurs, collecting three-phase voltage signals and current signals of the grid, and performing analog-digital conversion and low-pass filtering processing on the signals; converting the three-phase voltage and current to a target coordinate system through coordinate transformation, and calculating instantaneous reactive power; obtaining compensation current error by measuring the difference between load current and reference current calculated based on instantaneous reactive power theory; constructing an improved sliding surface based on the compensation current error, and updating adaptive gain coefficients in real time; calculating the control law according to the adaptive gain coefficients to complete the voltage sag fast response control.

[0009] In an implementation manner, when the modules are operated in parallel, the state space model is established based on the voltage and current sensor measurement values, the coupling weight coefficient is set, the optimization objective function is constructed, and the control sequence is obtained through iterative calculation, comprising: when the modules are operated in parallel, the DC side voltage is measured by using the voltage sensor, the AC side current is measured by using the Hall current sensor, and the power module state space model is established; the topological structure is determined by measuring the physical connection relationship between the modules, and the coupling weight coefficient between the modules is set; a distributed optimization objective function containing state tracking error items, control quantity constraint items and control increment constraint items is constructed; the optimization objective function is iteratively calculated by using the fast gradient method until the convergence condition is met, and the optimal control sequence is output.

[0010] In an implementation manner, the system key parameters are monitored, the fault module signal is blocked and the power is redistributed when the preset threshold is exceeded, and the system operation is restored after recording the fault information, comprising: real-time monitoring of DC side voltage, AC side current, device temperature and zero sequence current, judging whether the preset protection threshold is exceeded; when it is detected that any parameter exceeds the preset protection threshold, the pulse signal of the fault module is blocked, and the corresponding bypass switch is closed; the power distribution scheme of the remaining power module is recalculated, the fault information is sent to the target system; the fault type, fault time and related parameters are recorded, the fault handling is completed, and the system operation is restored.

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

[0012] In an implementation manner, the power module state space model is established, comprising: wherein x i (k+1) represents the state vector of the i-th module at the k+1 time, x i (k) represents the state vector of the i-th module at the k time, A represents the system state matrix, B represents the control input matrix, u i (k) represents the control input of the i-th module at the k time, represents a module set 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 j-th module at the k time.

[0013] In an implementation manner, the distributed optimization objective function including the state tracking error term, the control quantity constraint term and the control increment constraint term is constructed, including: wherein, 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 future p th step, x ref represents the state reference value, u i (k+p|k) represents the control input prediction of the i th module at time k for the future p th step, Δu i (k+p|k) represents the control input increment, and k represents the current discrete time step.

[0014] In a second aspect, the embodiments of the present application further provide an intelligent control system of a static reactive power generator module, which is used to implement the intelligent control method in any of the above embodiments. The system comprises a configuration unit, a control unit and a monitoring unit. The configuration unit is configured 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 configured to collect and process grid three-phase signals when voltage sag occurs, calculate reactive power through coordinate transformation, build a sliding mode surface and update gain coefficients to output a control law for voltage sag response. When the module is operated in parallel, a state space model is established based on voltage and current sensor measurements, a coupling weight coefficient is set, an optimization objective function is built and a control sequence is obtained through iterative calculation. The monitoring unit is configured to monitor key system parameters, block fault module signals and redistribute power when the parameters exceed a preset threshold, and restore system operation after recording fault information.

[0015] In a third aspect, the embodiments of the present application further provide an intelligent control device of a static reactive power generator module, which comprises a processor, a memory and a system bus. The processor and the memory are connected through the system bus. The memory is configured to store one or more programs, and the one or more programs include instructions which, when executed by the processor, cause the processor to execute the method in any of the above embodiments.

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

[0017] The intelligent control method, system and device of the static reactive generator module of the application can quickly calculate and output the control law under the voltage sag condition by introducing adaptive gain in the sliding mode surface, significantly improving the dynamic response speed of the system and reducing the compensation lag time. The real-time updating mechanism of the adaptive gain coefficient makes the system have stronger robustness when facing voltage mutation, effectively suppressing the steady-state error. The distributed predictive control strategy fully considers the coupling relationship between the modules, realizes the balanced distribution of power between the modules by constructing the state space model and the 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, improving the overall compensation performance of the system. The establishment of the 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 fault propagation and improve 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 the stable operation of the system and improving the fault tolerance of the system. Through the cooperative control and accurate modeling between the 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 the accurate control algorithm and optimized power distribution scheme, the system can reduce the operating loss while ensuring the compensation effect, improving the system efficiency. The distributed control strategy enables the system to maintain good stability and consistency when multiple modules are operated in parallel, avoiding the problems of circulating current and uneven load distribution between the modules. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0019] Figure 1 A flowchart of an intelligent control method of a static reactive generator module according to an embodiment of the application is shown in the figure.

[0020] Figure 2 A flowchart of a static reactive generator module voltage sag response method according to an embodiment of the application is shown in the figure.

[0021] Figure 3 A flowchart of a static reactive generator module parallel operation method according to an embodiment of the application is shown in the figure.

[0022] Figure 4 A schematic block diagram of an intelligent control system of a static reactive generator module according to an embodiment of the application is shown in the figure.

[0023] Figure 5 A schematic graph of the parallel performance of a static var generator is provided for an embodiment of the 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, numerical expressions, and numerical values of components and steps set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[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, and do not represent any specific technical meaning, nor do they represent the inevitable logical sequence between them. It should also be understood that in the embodiments of the present disclosure, "multiple" can mean two or more, and "at least one" can mean one, two, or more. It should also be understood that for any component, data, or structure mentioned in the embodiments of the present disclosure, unless specifically limited or given the opposite implication by the context, it can be understood as one or more in general. In addition, the term "and / or" in the present disclosure is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B together, and the existence of B alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the front and rear associated objects. It should also be understood that the description of each embodiment of the present disclosure emphasizes the differences between each embodiment, and the same or similar parts can be referred to each other, and for the sake of brevity, they will not be repeated.

[0026] It should also be understood that, for the sake of brevity, the description herein focuses on variations that differ from the embodiments described. It will be apparent to those skilled in the art that aspects of the present disclosure can be implemented in various ways and that the various embodiments can have common features. It is to be understood that the same or similar subject matter can be described in different patents. Accordingly, features or aspects of the disclosed embodiments can be combined with each other, unless specifically noted otherwise.

[0027] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0028] Figure 1 A flowchart of an intelligent control method of a static var generator module provided in the embodiments of the present application is shown. It should be noted that the static var generator (SVG) is a new type of dynamic var compensation device, mainly composed of a power unit, a control system and connection equipment. The power unit used in the embodiments of the present application is a three-phase H-bridge series structure, each phase containing 4 IGBT power modules (model selected as ABB 5SNA1600N170100, rated voltage 1.7 kV, rated current 1600 A) and corresponding driving circuits. The control system adopts a dual-core architecture of DSP+FPGA, wherein the DSP selects TI TMS320F28377D with a main frequency of 200 MHz, and the FPGA selects Xilinx XC7A50T-2FGG484.

[0029] It should be understood that in the prior art, the SVG realizes var power compensation by controlling the phase and amplitude of the alternating side voltage of the bridge circuit. Specifically, the reference current is calculated by mainly using the instantaneous var power theory, and then the PWM wave is generated by current tracking control to drive the power device switching. This control method has two main technical problems:

[0030] Under the condition of grid voltage sag, since the traditional control algorithm only considers the compensation characteristics under the steady state operating condition, the dynamic response characteristics at the voltage sag moment are not optimized, resulting in slow response of the SVG when the voltage sag occurs, and even the SVG stops running out of the grid. Specifically, when the controller calculates the compensation current, the sudden change of the grid fundamental component caused by the voltage sag causes a large error in the calculation of the var power based on the conventional Park transformation, and the compensation is not timely.

[0031] When multiple power modules are operated in parallel, since the DC side voltages of the modules are inconsistent, and the traditional control does not fully consider the influence of the inter-module circulating current, the load distribution among the modules is uneven. Specifically, the DC side capacitor voltage of each half-bridge unit fluctuates greatly, there is circulating current loss among the modules, and the overall compensation effect of the system is affected.

[0032] To solve the above technical problems, the embodiments of the present application disclose an intelligent control method of a static var generator, as shown in Figure 1As shown, at step S101, the rated parameters of the system, the sliding mode control parameters, the equivalent parameters of the system and the weight matrix parameters are written to complete the parameter initialization and configuration. Among them: the rated voltage parameters, the rated capacity parameters of the system, the sampling frequency parameters and the carrier frequency parameters of the system are obtained, and the parameters are written into the controller storage unit; the sliding mode control normal number, the nonlinear factor and the adaptive adjustment coefficient are written into the controller storage unit, which is used to build the sliding mode surface equation; the system equivalent parameters and the prediction step are configured, which are used for iterative calculation of the state prediction equation; the state tracking error term, the control amount constraint term and the control increment constraint term are weighted and configured according to the weight matrix parameters.

[0033] According to the above embodiment of the application, through parameter initialization and configuration, 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 build a sliding mode surface equation that is more suitable for transient operating conditions, which can improve the dynamic response speed and robustness of the system under voltage sag and other transient operating conditions. By configuring the system equivalent parameters and the prediction step, and based on these parameters, the state prediction equation is iteratively calculated, which can more accurately predict the system state and optimize the control effect. The control target is weighted and configured by the weight matrix parameters to ensure that each module remains consistent during the control process, thereby improving the overall performance and stability of the system.

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

[0035] For the core parameter configuration in the control algorithm, in the sliding mode control parameters: the normal number c=2.357, which is used to build the sliding mode surface S(t)=2.357×i1(t)+∫[…]dt; the nonlinear factor a=0.783, which is applied to the calculation of the nonlinear term |i1(t)| 0.783 ; the adaptive adjustment coefficients g1=0.0467 and g2=0.0325, which are used for gain update equations k1(t)=0.0467×|S(t)|×|i1(t)| and k2(t)=0.0325×|S(t)|×|i1(t)| 0.783 ; the system equivalent parameters R=0.235Ω and L=3.27mH, which are applied to the control law calculation

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

[0037] At step S102, when voltage sag (as shown in Figure 2 , the grid three-phase voltage signal is 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 control law is output to respond to voltage sag.

[0038] It includes: when voltage sag, collect the grid three-phase voltage signal and current signal, and perform analog-digital conversion and low-pass filter processing on the signal; convert the three-phase voltage and current to the target coordinate system through coordinate transformation, and calculate the instantaneous reactive power; obtain the compensation current error by measuring the difference between the load current and the reference current calculated based on the instantaneous reactive power theory; construct an improved sliding mode surface based on the compensation current error, and update the adaptive gain coefficient in real time; calculate the control law according to the adaptive gain coefficient, and complete the voltage sag fast response control.

[0039] At step S103, when the module is operating in parallel (as shown in Figure 3 , the state space model is established based on the voltage and current sensor measurement values, the coupling weight coefficient is set, the optimization objective function is constructed, and the control sequence is obtained through iterative calculation.

[0040] It includes: when the module is operating in parallel, measure the DC side voltage using a voltage sensor and measure the AC side current using a Hall current sensor, and establish a power module state space model; 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; use fast gradient method to iteratively calculate the optimization objective function until the convergence condition is met, and output the optimal control sequence.

[0041] At step S104, the system key parameters are monitored, the fault module signal is blocked when the preset threshold is exceeded, and the system operation is resumed after recording the fault information. It includes: real-time monitoring of DC side voltage, AC side current, device temperature and zero sequence current, judging whether the preset protection threshold is exceeded; 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 distribution scheme of the remaining power module is recalculated, and the fault information is sent to the target system; record the fault type, fault time and related parameters, complete the fault handling and restore the system operation.

[0042] In one embodiment, to ensure the safe operation of the system, the following protection functions are required. 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℃, the protection is triggered. Grounding protection: the zero sequence current protection method is adopted, and the operating current is set to 5A. When a power module failure is detected, the control system processes as follows: immediately block the PWM signal of the fault module; close the corresponding bypass switch; re-distribute the power of the remaining module; report the fault information to the upper control system.

[0043] Figure 2 A flowchart of a static var generator module voltage sag response method provided by the embodiment of the application is shown.

[0044] As shown in Figure 2 At step S201, when voltage sag occurs, 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 processing. 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 sampling values are sent to the DSP after being converted by a 16-bit AD converter (model ADS8568). The sampling data are processed by a digital low-pass filter, and the cutoff frequency is set to 2kHz.

[0045] At 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] At step S203, 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.

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

[0048] Specifically, after the compensation current error i1(t) is calculated, it is obtained by the difference between the real-time measured load current 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 through Clark transformation: Then, the instantaneous reactive power q is calculated: q = v α i β -v β i α . Based on this, an improved sliding mode surface S(t) is constructed. In the DSP, the following calculation is performed using a floating-point operation unit: S(t) = 2.357i1(t) + ∫[k1(t)sgn(i1(t))+k2(t)|i1(t)| 0.783 ]dt, wherein the adaptive gain coefficients k1(t) and k2(t) are updated in real time through the following formulas: k1(t) = 0.0467x|S(t)|x|i1(t)|, k2(t) = 0.0325x|S(t)|x|i1(t)| 0.783 .

[0049] At step S205, the control law is calculated according to the adaptive gain coefficients, and the voltage sag fast response control is completed. Finally, the control law

[0050] Figure 3 A flowchart of a parallel operation method of a static var generator module provided by an embodiment of the present application is shown. For the scenario of parallel operation of multiple power modules, a distributed predictive control strategy is used to realize coordination between the modules, and the specific implementation steps are as follows:

[0051] As Figure 3 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: wherein x i (k+1) represents the state vector of the i th module at the k+1 th moment, x i (k) represents the state vector of the i th module at the k th moment, 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 the k th moment, represents a module set adjacent to module i, w ij represents the coupling weight between module i and module j, and xj (k) represents the state vector of the jth module at time k.

[0052] Specifically, first, 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 . The 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, based on the measured data, an inter-module coupling prediction model is established. For the ith power module, its discrete state equation is:

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

[0054] At step S303, a distributed optimization objective function is constructed, which includes a state tracking error term, a control amount constraint term and a control increment constraint term. It includes: Wherein, J i represents the optimization objective function of the ith 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 ith module at time k for the future p steps, x ref represents the state reference value, u i (k+p|k) represents the control input prediction of the ith module at time k for the future p steps, Δu i (k+p|k) represents the control input increment, and k represents the current discrete time step.

[0055] At 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 optimization problem can be solved by using an improved fast gradient method, and the specific steps are: initializing the control sequence u i (k); calculating the objective function value J i and the gradient Using the Armijo criterion to determine the step size a; updating the control sequence: Judging whether the convergence condition ||u i (k+1)-u i (k)||<0.001 is met; if not, return to calculate the objective function value J i and the gradient Otherwise output the optimal control sequence.

[0056] Figure 4 A schematic block diagram of an intelligent control system of a static reactive power generator module is provided for the embodiments of the present application. It should be understood that the system shown in the figure is exemplary and not 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 an expression to clearly describe the relevant 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 of a specific form.

[0057] The present application discloses an intelligent control system of a static reactive power generator module, which is used to realize the intelligent control method as described in the above embodiments, and the system comprises 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 grid three-phase signals when voltage sag occurs, calculate reactive power through coordinate transformation, build a sliding mode surface and update gain coefficients to output control law for voltage sag response; when the module is operated in parallel, a state space model is established based on voltage and current sensor measurement values, a coupling weight coefficient is set, an optimization objective function is built and a control sequence is obtained through iterative calculation; the monitoring unit 403 is used to monitor key system parameters, block fault module signals and redistribute power when exceeding a preset threshold, and restore system operation after recording fault information.

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

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

[0060] In two-phase stationary coordinate system, the instantaneous reactive power is calculated. The error signal is obtained by comparing the calculated instantaneous reactive power value with the given value. 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 sliding surface is constructed in the form of integral term, which can effectively suppress the steady-state error of the system. The design of control law introduces nonlinear factor and adaptive gain, which improves the dynamic response performance of the system.

[0061] Based on the calculated control quantity, the PWM driving signal is generated by space vector PWM algorithm inside FPGA. The symmetric modulation method is used to generate the PWM signal, and the carrier frequency is set to 2.5 kHz. Considering the influence of dead time, 2 μs dead delay is added to the rising edge and falling edge of the PWM signal. The PWM signal is transmitted to the IGBT driving circuit through optical fiber. The driving circuit uses double isolation power supply, and the driving power supply provides +15 V / -8 V driving voltage by using DC / DC module.

[0062] The driving and protection of IGBT power module are the key to the stable operation of the system. The driving circuit uses double interlocking form, which is realized by a special IGBT driving chip. The driving chip integrates short-circuit protection, over-current protection and under-voltage lockout protection functions. The short-circuit protection uses the method of detecting VCE voltage, and the protection threshold is set to 5.5 V, with a response time less than 1 μs. The over-current protection is based on the sampling value of Hall current sensor, and the protection threshold is set to 1.5 times of the rated current. The under-voltage lockout voltage is set to 12 V, which ensures that the IGBT works in the safe region.

[0063] To improve the reliability of the system, a perfect fault diagnosis and handling mechanism is also added in the control system. The fault diagnosis mainly includes IGBT failure detection, DC bus voltage anomaly detection and system over-temperature detection, etc. The IGBT failure detection is based on VCE voltage and gate feedback signal. When the 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 anomaly detection uses voltage sensor for real-time monitoring. When the voltage exceeds 1.2 times of the rated value or is lower than 0.8 times, the protection is triggered. The system over-temperature detection measures the IGBT junction temperature through the thermistor. When the temperature exceeds 125℃, the protection is triggered.

[0064] In the design of the heat dissipation system, the combination of air cooling and water cooling is adopted. The cooling liquid of the water cooling system uses deionized water with a flow rate of 20L / min, and the water inlet temperature is controlled below 25℃. The air cooling system uses a variable speed fan, which automatically adjusts the speed according to the IGBT junction temperature. Through this composite cooling method, the IGBT junction temperature can be controlled below 80℃, ensuring the 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 communication between the two controllers is through CAN bus with a communication period of 10ms. The system-level controller calculates the optimal power distribution scheme in real time according to the load characteristics and system operating state, and sends the command to each module. After receiving the command, the module-level controller executes the corresponding control strategy according to the local measurement data and feeds back the operating state 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 communicate through the communication bus, improving the flexibility and scalability of the system.

[0066] In actual application, the startup process of the system needs to be strictly followed in sequence. First, power the control system, wait for DSP and FPGA to complete initialization; then power the drive circuit, check if each drive signal is normal; finally, power the power unit, gradually boost to the rated voltage. The whole startup process takes about 30s, and after the startup is completed, the system enters the normal operating state. The shutdown process needs to reduce the power output first, and then gradually power off in reverse order. This strict start-stop program design can effectively prevent voltage and current surges during startup and shutdown, and improve the service life of the system.

[0067] Figure 5A schematic curve diagram of the parallel performance of a static var generator is provided for the embodiments of the present application. For the parallel operation characteristics of the static var generator, in-depth analysis and performance verification are carried out from the actual application point of view. In the actual operation process, through the establishment of a distributed predictive control model, the response characteristics of the system are comprehensively tested and analyzed. In the test process, first, a single module operation mode is adopted, the DC side voltage data is collected by using a high-precision voltage sensor LV25-P, and the AC side current data is obtained by using a Hall current sensor LA55-P, and a benchmark performance curve is established. Then, switch to the multi-module parallel operation mode, and compare and analyze the dynamic response characteristics under the two working modes.

[0068] In the system startup stage, first, the inter-module communication link test is carried out. The communication network is built through an industrial-grade Ethernet switch, and the data interaction is realized through TCP / IP protocol. The communication cycle is set to 100 μs. After the communication test is completed, the parameter self-tuning stage is entered. In this stage, the system response data is collected by injecting standard test signals, and the system equivalent parameters are identified by using the least square method. According to the identification result, the controller parameters are automatically calculated and updated, including the normal number c value and the nonlinear factor a value in the sliding mode control, and the specific values of the weight matrix Q, R and S in the predictive control.

[0069] In the steady-state operation stage, the system shows significant performance difference. When a single module is running, the system response speed is fast, but there is a certain overshoot, and from the curve it can be seen that there is an obvious response inflection point at 10 ms, and the overshoot reaches 15% of the rated value. By introducing the improved adaptive damping sliding mode control, the system gradually tends to be stable after 20 ms, 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 shows 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 the dynamic power distribution among the modules, the system can more flexibly cope with load fluctuations. When a sudden load change is detected, the distributed controller can quickly calculate the optimal power distribution scheme within the adjacent sampling period. Specifically in the control process, the system first acquires the instantaneous reactive power value in the αβ coordinate system based on the real-time acquisition of the three-phase voltage and current signals of the power grid through Clark transformation. Then, the predictive controller predicts the future state trajectory with a span of 3 sampling periods. On this basis, by solving the distributed optimization objective function, the coordinated control of the output power of each module is realized.

[0071] For system reliability improvement, dynamic redundancy design is also introduced in the multi-module parallel scheme. 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 the IGBT junction temperature exceeds 125℃ 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 fault module and closes the bypass switch. At the same time, the distributed predictive controller automatically recalculates the coupling weight coefficient w_ij, updates the state space model of each power module, and ensures that the system quickly transitions to a new steady-state operating point.

[0072] In terms of harmonic control effect, the multi-module parallel operation scheme also shows significant advantages. Through the coordinated control between modules, the system can more effectively suppress high-order harmonics. Test data shows that under the rated operating condition, 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, which realizes accurate compensation of each harmonic through accurate modeling and dynamic optimization.

[0073] Further, the embodiment of the present application also provides an intelligent control device of a static var generator module, comprising: a processor, a memory, a system bus; the processor and the memory are connected through the system bus; the memory is used for storing one or more programs, the one or more programs include instructions, the instructions make the processor execute any of the above methods when executed by the processor.

[0074] Further, the embodiment of the present application also provides a computer program product, which, when running on a terminal device, causes the terminal device to execute any of the above methods.

[0075] From the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the above embodiment methods can be implemented by means of software and the necessary general hardware platforms. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) execute the methods described in the various embodiments or some parts of the embodiments of the present application.

[0076] It should be noted that the various embodiments described in the specification are intended to be exemplary only and that the scope of the application is not intended to be limited to the embodiments described in the specification. Rather, the scope of the present application is intended to cover all modifications and variations of these embodiments and other embodiments that fall within the scope of the present application. It is apparent that the features and components of the present application can be combined together or separated into further components to produce the novel features and combinations of components that possess the benefits of the present application. It is therefore contemplated to cover any possible designs that fall within the scope of the present application.

[0077] It should also be noted that, in the specification, relational terms such as first and second, and the like, can be used solely to distinguish one from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0078] The above description of disclosed embodiments provides information sufficient to understand how to make and use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without the use of the innovative principles of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed in the specification.

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, including: The system's rated voltage parameters, rated capacity parameters, sampling frequency parameters, and carrier frequency parameters are obtained, and the parameters are written into the controller's storage unit. The sliding mode control constants, nonlinear factors, and adaptive adjustment coefficients are written into the controller storage unit to construct the sliding mode surface equation; Configure the system equivalent parameters and prediction step size for iterative calculation of the state prediction equation; The state tracking error term, control constraint term, and control increment constraint term are weighted according to the weight matrix parameters. When a voltage sag occurs, the three-phase signals of the power grid are acquired and processed. Reactive power is calculated through coordinate transformation, a sliding mode surface is constructed and the gain coefficient is updated, and a voltage sag response is generated using an output control law, including: When the voltage drops, the three-phase voltage and current signals of the power grid are collected, and the signals are processed by analog-to-digital conversion and low-pass filtering. The three-phase voltage and current are transformed to the target coordinate system using 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 mode surface is constructed based on the compensation current error, and the adaptive gain coefficient is updated in real time; The control law is calculated based on the adaptive gain coefficient to achieve fast response control of voltage sag. When the modules are running in parallel, a state-space model is established based on the measured values ​​of voltage and current sensors, coupling weight coefficients are set, an optimization objective function is constructed, and the control sequence is obtained through iterative calculation. The system monitors key parameters, and when these parameters exceed preset thresholds, it blocks the signals of faulty modules, redistributes power, records fault information, and then restores system operation.

2. The intelligent control method according to claim 1, characterized in that, in, When modules operate in parallel, a state-space model is established based on voltage and current sensor measurements, 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 running 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 to establish a state space model of the power module. Measure the physical connections between modules to determine the topology and set the coupling weight coefficients between modules; Construct a distributed optimization objective function that includes state tracking error terms, control quantity constraint terms, and control increment constraint terms; The fast gradient method is used to iteratively calculate the objective function until the convergence condition is met, and the optimal control sequence is output.

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

4. The intelligent control method according to claim 1, characterized in that, in, An improved sliding surface is constructed based on compensation for current error, including: S(t)=2.357i1(t)+∫[k1(t)sgn(i1(t))+k2(t)|i1(t)| 0.783 ]dt, Where S(t) represents the compensation current error, i1(t) represents the adaptive gain coefficient, k1(t) and k2(t) represent the sign function.

5. The intelligent control method according to claim 2, characterized in that, in, Establish the state-space model of the power module, including: Where, 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. w represents the set of modules adjacent to module i. ij x represents the coupling weight between module i and module j. j (k) represents the state vector of the j-th module at time k.

6. The intelligent control method according to claim 2, characterized in that, in, Construct a distributed optimization objective function that includes state tracking error terms, control quantity constraint terms, and control increment constraint terms, including: Among them, J i Let N represent the optimization objective function for the i-th power module. p Indicates the prediction time domain length, p represents the step size index, and x represents the step size index. i (k+p|k) represents the state prediction of the i-th module at time k for the p-th future step, x ref Indicates the state reference value, u i (k+p|k) represents the prediction of the control input of the i-th module at time k for the p-th future step, Δu i (k+p|k) represents the control input increment, where k represents the current discrete time step.

7. An intelligent control system for a static var generator module, used to implement the intelligent control method as described in any one of claims 1-6, characterized in that, The system includes: a configuration unit, a control unit, and a monitoring unit; wherein, The configuration unit is used to write the 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 mode surface and update the gain coefficient, and output the control law to respond to the voltage sag. When the modules are running in parallel, a state space model is established based on the measurement values ​​of the voltage and current sensors, the coupling weight coefficient is set, an 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 the parameters exceed a preset threshold, it blocks the signal of the faulty module and redistributes the power. After recording the fault information, it restores system operation.

8. An intelligent control device for a static var generator module, characterized in that, include: Processor, memory, system bus; wherein the processor and the memory are connected via the system bus; The memory is used to store one or more programs, the one or more programs including instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-6.

Images