6 results about "Electrical dynamics" patented technology

The application discloses a motion control method of a nonlinear electromechanical servo system and relates to the technical field of electromechanical system management; a dynamic model of the nonlinear electromechanical servo system is established, the electrical dynamics of a motor current loop corresponding to the nonlinear electromechanical servo system is simplified into a proportional link; the uncertainty of the dynamic model of the nonlinear electromechanical servo system is approximately fitted by using a radial basis neural network; the strong external time-varying disturbance of the nonlinear electromechanical servo system is estimated by using an extended state observer in combination with the dynamic model; and the desired motion trajectory of the nonlinear electromechanical servo system is fed back by using an error sign robust integral control method according to the estimation result, so that the actual motion trajectory of the nonlinear electromechanical servo system is controlled.

The invention discloses a small-interference stability evaluation method and system for a network construction type converter system, and relates to the technical field of power system stability analysis, and the method comprises the steps: obtaining a plurality of network construction type converters of the network construction type converter system, constructing a corresponding network construction type converter model according to the electrical dynamic characteristics of each network construction type converter and a controller, and carrying out the small-interference stability evaluation of the network construction type converter system; and performing multi-working-condition simulation processing according to each network construction type converter model to obtain a corresponding damping training set, training a preset initial damping prediction model by adopting the damping training set to obtain a corresponding damping prediction model, and obtaining converter parameters of the network construction type converter system. And performing small-interference stability evaluation on the converter parameters according to the damping prediction model to obtain a corresponding evaluation result. The technical problems that an existing small-interference stability evaluation method is high in calculation complexity and poor in real-time performance, the online evaluation requirement cannot be met, and the operation reliability of a power system is reduced are solved.

The application belongs to the technical field of power electronic real-time simulation, and aims at the problems of insufficient simulation accuracy of traditional pure CPU simulation, limited simulation scale of FPGA simulation, communication bottleneck and poor flexibility of "CPU+FPGA" hybrid simulation, and proposes a power electronic converter small step real-time simulation method and system. The core is to build a multi-port equivalent model based on the switching function method or the improved node method, and to equivalent the nonlinear part of the converter into a controlled source and fuse the linear passive network; adopt the "CPU unique calculation + FPGA special communication" architecture, FPGA nanosecond level acquisition PWM pulse sequence transmission to CPU, CPU through the main step nesting small step recursive calculation, in the microsecond level main step, reproduce the hundred nanosecond level electrical dynamic. The matching special system deeply optimizes the CPU multi-core parallel ability, the communication cost is low, realizes the accuracy, the efficiency and the scale unification, the cost is low, the flexibility is high, can support the large-scale high switching frequency system high precision simulation, is applicable to the new energy grid connection, direct current transmission and other scenes.

The invention relates to the technical field of advanced adiabatic compressed air energy storage, in particular to an advanced adiabatic compressed air energy storage power generation side electromechanical coupling modeling method, which comprises the following steps: respectively establishing component dynamic models of key components, and determining mechanical characteristics; constructing a shafting motion equation by adopting a multi-section concentrated mass model, and determining a shafting model; describing lagging characteristics of the advanced adiabatic compressed air energy storage power generation side system through a first-order inertia model to determine an inertia link; and integrating the assembly dynamic model, the shafting model and the inertia link, and establishing an advanced adiabatic compressed air energy storage power generation side electromechanical coupling model in combination with a synchronous generator electromagnetic equation. Therefore, the problems that in the related technology, due to the fact that an existing advanced adiabatic compressed air energy storage modeling method cannot integrate thermodynamic characteristics and shafting mechanical characteristics, power generation side mechanical-electrical dynamic interaction is difficult to accurately describe, and then reliable model support is difficult to provide for shafting oscillation analysis and system stability optimization are solved.

The invention relates to a six-axis synchronous error prediction and cooperative compensation method for a heavy-load equal material forming robot. The method comprises the following steps: S1, establishing a kinematics model, a mechanical dynamics model, an electrical dynamics model and an electromechanical coupling dynamics model of the heavy-load equal material forming robot; s2, building a heavy-load equal material forming robot collaborative error prediction model, wherein the heavy-load equal material forming robot collaborative error prediction model comprises a long short-term memory network prediction model, a physical model prediction model and an LSTM-physical model fusion prediction model; and S3, building a heavy-load equal-material forming robot cooperative control model, wherein the heavy-load equal-material forming robot cooperative control model comprises a three-ring PI control model, a traditional cooperative error control model and a cooperative control model based on an LSTM-physical model fusion prediction method. According to the method, the collaborative error of the permanent magnet synchronous motor is predicted based on the LSTM-physical model fusion prediction method, and the control precision of the heavy-load equal material forming robot can be improved. In order to realize accurate prediction, an LSTM method is used to suppress system noise, and a physical principle is adopted to improve robustness under the condition of forming a load.

The application discloses a power distribution switch control system based on a smart power grid, and belongs to the technical field of electric energy transmission and distribution and control. The system comprises the following steps: acquiring a synchronous acquisition waveform pair, performing feature extraction, cross correlation calculation and singular value decomposition, and generating a damping loss factor; constructing a basic topology adjacency matrix, calculating a load rate thermal stability evaluation value, performing node stiffness assignment, generating a dynamic topology stiffness model, performing structure stress deduction, generating a topology stiffness degradation distribution diagram, performing screening, obtaining a backup support node set and marking an external non-support path to obtain a stiffness defect isolation band; constructing an isolation action matrix, and generating a cooperative action instruction sequence. The application adopts the means of fusing switch mechanical and electrical dynamic characteristics and power grid topology structure, constructing a dynamic physical model and performing stress deduction, can prospectively evaluate the structural health degree of the overall power distribution network, and generates a cooperative action instruction, so as to actively eliminate potential risks and improve operation stability.