22 results about "Finite control set" patented technology

The invention relates to the technology of power electronics and electric energy quality control, in particular to an energy storage type UPQC and switch state prediction control method based on improved model prediction control, which comprises a series active filter, a parallel active filter, a direct current bus, an energy storage system and a controller, according to the control method, an energy storage power and charge state dynamic equation is introduced into an MPC prediction model, and a multi-objective cost function is designed. And the controller predicts and evaluates the candidate switching vector in each sampling period based on a finite control set model predictive control strategy, and outputs an optimal switching state through value function optimization, thereby realizing collaborative optimization of electric energy quality management and energy storage energy management. According to the method, the dynamic response capability and compensation precision of the UPQC under the working conditions of voltage sag / sag, imbalance, harmonic waves and load fluctuation are remarkably improved, meanwhile, the energy storage utilization efficiency and the system stability are improved, and the method is suitable for a power distribution network with new energy high permeability and large load fluctuation.

The invention discloses a permanent magnet synchronous motor wide speed range model prediction control method and system based on a mean value reduction optimization algorithm, and belongs to the technical field of motor control. According to the method, a discrete mathematical model of the permanent magnet synchronous motor is established under a d-q rotating coordinate system, a q-axis current reference value is generated by an outer ring through a speed PI regulator, and a d-axis current reference value is calculated in combination with a maximum torque per ampere (MTPA) principle and a weak magnetic region voltage constraint; finite control set model prediction control is adopted in an inner ring, enumeration prediction is conducted on candidate voltage vectors of the three-phase voltage source type inverter, a cost function containing a d-axis current error, a q-axis current error and an electromagnetic torque error is constructed, an optimal voltage vector is selected, and a three-phase PWM signal is generated through space vector pulse width modulation to drive a motor. The system is provided with a mean value reduction optimization module, joint optimization is carried out on speed outer ring PI parameters and cost function weight coefficients in the off-line stage, the parameters are stored in a lookup table, and automatic setting of control parameters is achieved. The permanent magnet synchronous motor can improve the dynamic response performance of the rotating speed and the current in the base speed area and the weak magnetic area, reduce the total harmonic distortion of the stator current, improve the voltage utilization rate and the overall operation efficiency of the permanent magnet synchronous motor, and is suitable for occasions such as electric vehicle driving and high-performance servo systems.

The application provides a motor field weakening control method and system based on limited set model predictive control, can solve the problem that the output voltage of limited control set model predictive control cannot be directly used for closed-loop field weakening control, and comprises the following steps: obtaining operation parameters of a current sampling period of a motor, and predicting dq axis currents of a next sampling period; constructing a reference voltage of the next sampling period, the reference voltage is divided into a sampling voltage component and a transient voltage component, performing boundary judgment on a reference current vector corresponding to the transient voltage component, and determining whether to perform dynamic amplitude limiting processing on the transient voltage component according to a boundary judgment result; adding the sampling voltage component and the processed transient voltage component to obtain a corrected reference voltage, then obtaining a field weakening current component through comparison and closed-loop feedback, superimposing the field weakening current component to obtain a corrected d-axis reference current, and updating the reference current; and obtaining an optimal voltage vector through an evaluation function of limited set model predictive control to perform control.

The application discloses a kind of three-phase unbalanced power grid under pulse width modulation rectifier coordination control method and system, and it is related to power electronics and electric energy conversion control technical field.The method comprises: collecting three-phase voltage and current and DC bus voltage;By positive and negative sequence separation, construct unbalance degree index κ;With outer loop active power reference generated by active disturbance rejection controller, after amplitude limiting and slope limiting processing, it is sent into inner loop limited control set model predictive controller;Introduce κ-saturation flag joint gain scheduling mechanism on-line adjustment observer bandwidth and feedback gain, and set saturation fallback state machine to freeze integral channel;Inner ring uses two-step prediction, DC bus state perception, reactive power weight soft scheduling and reactive power average value offset removal.This application converts power grid unbalance degree into control parameter on-line setting basis, realizes outer ring safety constraint and inner ring prediction scheduling coordination, significantly suppresses DC bus voltage fluctuation and reactive power pulsation, improves system robustness and embedded realizability.

This invention provides a control method and apparatus for a 4L-NNPC inverter based on an improved finite control set model. The method includes: real-time acquisition of the current three-phase grid voltage, current flying capacitor voltage, and current output three-phase current; calculation of the static reference current and a preliminary evaluation function; determination of a candidate switch state set through an output level screening mechanism, combining the preliminary evaluation function and the static reference current; for each candidate switch state, prediction of the corresponding state flying capacitor voltage and state output three-phase current at the next moment based on the current output three-phase current; calculation of the current tracking cost function and the balancing flying capacitor voltage cost function using a multi-objective cost function based on sliding mode control theory, generating an overall cost function; and selection of the candidate switch state that minimizes the overall cost function as the optimal control output for the 4L-NNPC inverter control. This invention significantly reduces computational complexity while improving system dynamic performance and steady-state accuracy.

This invention discloses a neural network-based active disturbance rejection (ADRR) composite control method and system for permanent magnet synchronous motors (PMSMs). The method includes: constructing a second-order PMSM mathematical model; constructing a second-order nonlinear ADRR controller; generating a first control quantity based on the second-order nonlinear ADRR controller according to position commands and initial position feedback, and inputting the first control quantity into a current loop controller to control the PMSM; adjusting and optimizing the second-order nonlinear ADRR controller using a radial basis function neural network based on current position feedback and position commands; generating a second control quantity based on the adjusted and optimized second-order nonlinear ADRR controller according to position commands and current position feedback; predicting multiple sets of switching state combinations based on a dual-vector finite control set model using the second-order PMSM mathematical model and the second control quantity, and selecting the optimal switching state combination from the multiple sets of switching state combinations; and controlling the PMSM according to the optimal switching state combination. This significantly improves the real-time performance and robustness of the system.

The application provides a motor control method and device, and the method comprises the following steps: obtaining motor parameters and identification parameters of a motor based on a prediction period, wherein at least part of the identification parameters can be updated based on real-time working conditions of the motor, and the update period is an integer multiple of the prediction period, the motor parameters comprise q-axis current, d-axis current and electric angular velocity, and the identification parameters comprise stator resistance, q-axis inductance, d-axis inductance and permanent magnet flux linkage; and control parameters of the motor are determined according to the motor parameters, the identification parameters and a limited control set model predictive control algorithm, so as to control the operation of the motor. Thus, by setting the identification parameters and updating the identification parameters based on the real-time working conditions, the model can be made to be close to the actual operation state of the motor in real time, so that the model mismatch problem caused by parameter drift can be effectively weakened, and the control precision of the limited control set model predictive control algorithm is improved.

The invention provides a 4L-NNPC inverter control method and device based on an improved finite control set model. The method comprises the steps of collecting a current three-phase voltage of a power grid, a current flying capacitor voltage and a current output three-phase current in real time; static reference current and a preliminary evaluation function are obtained through calculation; determining a candidate switch state set through an output level screening mechanism in combination with the preliminary evaluation function and the static reference current; for each candidate switch state, predicting a state flying capacitor voltage and a state output three-phase current corresponding to the current output three-phase current at the next moment according to the current output three-phase current; a multi-target cost function based on a sliding mode control theory is adopted to calculate a current tracking cost function and a balanced flying capacitor voltage cost function, and an overall cost function is generated; and selecting a candidate switch state which enables the total cost function to be minimum as the optimal control output of the 4L-NNPC inverter. According to the method, the dynamic performance and the steady-state precision of the system are improved while the calculation complexity is remarkably reduced.

The embodiment of the invention discloses a modular multilevel converter sub-module fault-tolerant operation method and a modular multilevel converter sub-module fault-tolerant operation system. The method comprises the following steps: identifying and processing a sub-module fault in an MMC system, and adjusting the capacitor voltage of the remaining sub-modules so as to ensure the stability of the direct current side voltage when the sub-module fault occurs; under the condition that the direct current side voltage is stable, alternating current tracking is set to be the highest priority, and an optimal switching sequence is searched in a limited control set by establishing a mathematical model and a design value function; after current tracking is completed, interphase circulating current suppression is selected as a secondary target, and the circulating current influence is minimized by using discretization processing and a voltage compensation technology; and after the secondary target is optimized, a sorting voltage-sharing algorithm is implemented, and the input or bypass state of each sub-module is monitored and dynamically adjusted in real time. By implementing the method provided by the embodiment of the invention, not only can the operation speed be improved, but also an efficient fault-tolerant operation scheme is provided for MMC sub-module faults, and the reliability and the performance of the system are remarkably improved.

The invention discloses a grid-connected inverter fault ride-through method and system considering symmetric and asymmetric faults, and relates to the technical field of electric power, and the method comprises the steps: obtaining power grid voltage measurement data and power grid current measurement data, and carrying out the fault ride-through of a grid-connected inverter based on the power grid voltage measurement data and the power grid current measurement data; generating reference voltage data and reference phase data through droop control; based on the power grid voltage measurement data and the reference voltage data, outputting through a finite control set model prediction control strategy to obtain a switching signal for driving an inverter switch; when the power grid current measurement data exceed a preset current rated value, judging that the current is in a fault state, calculating to obtain a virtual impedance value based on the fault state, and generating a voltage correction value based on the virtual impedance value and the power grid current measurement data to adjust the reference voltage data; and according to the adjusted reference voltage data, a switching signal for driving an inverter switch is updated, and accurate current constraint under symmetric and asymmetric faults is realized.

The embodiment of the application discloses a modular multilevel converter submodule fault tolerance operation method and system. The method comprises the following steps: identifying and processing the submodule fault in the MMC system, adjusting the capacitor voltage of the remaining submodules to ensure the stability of the DC side voltage when the submodule fault occurs; under the condition that the DC side voltage is stable, the AC current tracking is set as the highest priority, the optimal switching sequence is found in the limited control set by establishing a mathematical model and designing a value function; after the current tracking is completed, the interphase circulating current suppression is selected as the secondary target, and the discrete processing and voltage compensation technology are used to minimize the circulating current influence; after the secondary target optimization, the sorting voltage sharing algorithm is implemented, and the input or bypass state of each submodule is monitored and dynamically adjusted in real time. Through the implementation of the method of the embodiment of the application, not only the operation speed is improved, but also an efficient fault tolerance operation scheme is provided for the MMC submodule fault, and the reliability and performance of the system are significantly improved.

The invention belongs to the technical field of renewable energy source grid connection, and provides a grid-connected converter cluster model prediction dual-mode control method and a grid-connected converter cluster model prediction dual-mode control system, which overcome the influence of line impedance difference by introducing a distributed compensation mechanism into a traditional control architecture, realize accurate reactive power equalization among converters in a network construction mode, and improve the reliability of the system. The hidden danger of power oscillation is eliminated; limited control set model prediction control is adopted to replace a traditional PI controller to serve as a unified inner ring, the smoothness and safety of the mode switching process are effectively improved, a complete cluster control framework is constructed by integrating collaborative switching, power coordination and rapid inner ring control, and the operation stability of the system under wide-range power grid impedance changes is comprehensively improved.

The invention belongs to the technical field of bid invitation management, and discloses a swimming pool heat pump SCOP working condition optimal energy efficiency debugging control method based on multi-parameter dynamic optimization, and the method comprises the steps: S1, obtaining state data, and obtaining an adjustment boundary of a control quantity based on the state data; s2, constructing an initial candidate set based on the adjustment boundary, and obtaining a limited control action set based on the initial candidate set; s3, respectively acquiring an input vector corresponding to each group of control combinations in the limited control set, acquiring a predicted COP value based on the input vector, and acquiring an index sequence based on the predicted COP value; and S4, obtaining an alternative set based on the index sequence, respectively calculating a comprehensive score of each group of control combinations in the alternative set, obtaining an optimal control combination based on the comprehensive scores, and updating the control instruction based on the optimal control combination. According to the method, the instantaneous energy efficiency and the final SCOP of the swimming pool heat pump under multiple working conditions are remarkably improved, and the industrial pain points that the energy efficiency is low and the system is prone to oscillation due to traditional unified control logic are solved.

The invention relates to the technical field of converter control, and discloses a finite control set model prediction control method, device and equipment for a machine side converter, and the method comprises the steps: inputting the port voltage of the machine side converter at the current moment under a target coordinate and the stator current of a fan at the current moment under the target coordinate into a prediction model, the stator current of at least one future moment under the target coordinate is predicted through the prediction model; on the basis of the stator current of at least one future moment under the target coordinate and the machine side current reference value under the target coordinate, performing traversal calculation to obtain the optimal switching state of each bridge arm of the converter, which minimizes the value of the cost function; and controlling each bridge arm of the converter according to the optimal switching state. According to the method, the on-off state of each bridge arm of the machine side converter can be adaptively changed on line, tedious PI setting is reduced, decoupling is not needed, multiple times of conversion is not needed, and the operand is small.

The invention discloses an inverter control method and apparatus, an electronic device and a readable storage medium. The method comprises the steps of obtaining an inverter target element; constructing a current feature vector according to the target element, and taking the current feature vector as the input of a neural network; regularization is applied to the weight matrix of the neural network, and the bias parameter of the neural network is kept in a non-regularization state; constraining the neural network based on a preset loss function; the neural network forwards propagates and outputs a target control input voltage; and taking the target control input voltage as the input of a preset cost function, obtaining the on-off state of the inverter when the cost function is minimum, and controlling the operation of the inverter in the on-off state. According to the embodiment of the invention, the neural network is adopted, the mapping from the current to the target control input voltage is directly realized, the whole process does not need to depend on any prior system parameters such as inductance and resistance of the inverter, and the dependence of traditional finite control set model prediction control on an accurate mathematical model is thoroughly eliminated.

The application relates to power electronics and power quality treatment technology, and particularly relates to an energy storage type UPQC based on an improved model predictive control and a switching state predictive control method.The device comprises a series active filter, a parallel active filter, a DC bus, an energy storage system and a controller, a dynamic equation of energy storage power and a state of charge is introduced into an MPC prediction model in the control method, and a multi-target cost function is designed.The controller is based on a limited control set model predictive control strategy, candidate switching vectors are predicted and evaluated in each sampling period, and the optimal switching state is output through value function optimization, so that the collaborative optimization of power quality treatment and energy storage energy management is realized.The application significantly improves the dynamic response capability and compensation accuracy of the UPQC under voltage sag / surge, imbalance, harmonic and load fluctuation working conditions, improves the energy storage utilization efficiency and system stability, and is suitable for new energy high penetration and load fluctuation large distribution networks.

The application discloses a weak magnetic method of a permanent magnet synchronous motor finite control set model predictive control system based on a closed loop of expected fundamental voltage, and belongs to the technical field of permanent magnet synchronous motor speed regulation control. The application solves the problems of voltage saturation and poor robustness of the finite control set model predictive control system in a weak magnetic area. The application carries out one-step delay compensation on stator current, and brings the predicted stator current predicted value of the delay compensation into a voltage vector model under current vector deadbeat control, so that the voltage vector reference value after compensation can be calculated. The expected fundamental voltage vector is calculated based on the voltage vector reference value after compensation, and finally the expected fundamental voltage vector is used as the feedback of the closed loop weak magnetic control, so that the maximum voltage control can be realized, and voltage saturation in the weak magnetic area is avoided. The application has better current dynamic performance, faster acceleration time and better robustness. The application can be applied to the weak magnetic control of the permanent magnet synchronous motor.

The invention provides a permanent magnet synchronous motor neural network model predictive control system and a control method, and relates to the technical field of motor control and artificial intelligence crossing, and the method comprises the steps: firstly, collecting the stator current, DC bus voltage, inverter switching signals, motor operation temperature and other multi-dimensional physical quantities in a motor driving system; an advanced architecture of deep fusion neural network and finite control set model prediction control is designed. According to the architecture, an offline trained neural network is utilized, the state sensing ability of a system is dynamically adjusted on line, and full-speed-domain rotating speed and position identification without a physical speed sensor is achieved; and then through online real-time prediction, a weight factor and a voltage vector selection strategy of model prediction control are adaptively adjusted, parameter mismatch and external disturbance are effectively overcome, and the dynamic response speed and the control precision of the system are remarkably improved.

The application discloses a bearingless motor adaptive discrete-time model predictive control method combining time-varying phase change coordination domain and parameter identification, embedding torque phase change coordination domain and suspension force phase change coordination domain in a phase change interval, realizing non-uniform fine reconstruction of a motor operation area, and suppressing phase change current spikes from a physical level; introducing a model reference adaptive algorithm to identify winding parameters online, compensating parameter perturbation influence caused by temperature rise and magnetic saturation in real time; respectively constructing torque cost function and suspension force cost function, reducing dynamic coupling degree between subsystems from a control logic; formulating limited control set switching state optimization rules based on real-time rotor position to constrain the optimization range, determining the optimal switching state of a power converter, and acting on the bearingless motor. The application strictly guarantees rotor suspension stability, significantly reduces torque ripple and displacement deviation during phase change, and effectively improves the parameter robustness of the system.

The application discloses a modular multilevel converter limited control set model predictive control method, obtains a cost function, a modular multilevel converter prediction model, a first sub-module set, a first sub-module voltage set and a first current set; based on the first sub-module set and the first sub-module voltage set, a voltage sorting algorithm and an optimal sub-module selection algorithm are used for processing to obtain a second sub-module voltage set; based on the second sub-module voltage set and the first current set, the modular multilevel converter prediction model is used for processing to obtain a target prediction value set; based on the target prediction value set, the cost function is used for control to obtain a limited control set model predictive control result of the modular multilevel converter. Through the optimal sub-module selection algorithm, the number of sub-modules available for selection in the modular multilevel converter is reduced, the calculation amount of the limited control set model predictive control is reduced, and meanwhile, the output performance of the modular multilevel converter is ensured.

This invention discloses a speed control method, system, device, and storage medium for a doubly-fed induction generator (DFIG) system, belonging to the field of DFIG control technology. The invention acquires the DFIG's operating data and determines the current power and flux linkage data at the current moment based on this data; determines speed error data based on given and measured speed data, and determines active power reference data based on the speed error data; determines a target switching state based on the current power data, the current flux linkage data, the active power reference data, and a finite control set model predictive control algorithm; generates a control signal based on the target switching state, and controls the rotor-side converter based on the control signal. This achieves the beneficial effects of improving the dynamic performance of the DFIG and enhancing the quality of generated power.