In order to make the best use of solar energy and ensure that solar cells are always working close to the point of maximum output, A MPPT algorithm combining the fixed voltage method and the conduction enhancement method of the eddy current function is proposed to solve the problem of balancing accuracy and tracking speed in the current MPPT algorithm. The algorithm first adopts the fixed voltage method maximum power point monitoring quickly at startup, and then to increase the tracking speed and steady-state accuracy, the following stages use the method of optimizing the conductivity gain by the inverse tangent function. In this paper, the simulation results are compared with the traditional incremental conductance method in the same environment. The simulation results show that the tracking time and accuracy of the fixed voltage method and the improved variable step conductance increment method before and after the illumination change are 0.011s and 0.04 s and tracking accuracy is 0.3% and 0.1% after the illumination change are respectively. The tracking time and accuracy of the traditional conductance increment method before and after light change are 0.015s and 1.6%, respectively, after light change is 0.25s and 1.6%. Therefore, the control strategy of the conductance increment method optimized based on the fixed voltage method and the inverse tangent function has faster tracking speed and steady state accuracy.
All kinds of distributed devices and intelligent devices are connected to the power system, which makes the power system more and more sensitive to the fluctuation of power, which leads to the identification and processing of power quality disturbance (PDQ) becoming more and more important. Aiming at the problem of composite disturbance classification and identification with multiple single power quality disturbances, a composite power quality disturbance identification method based on improved S-transformation is proposed in this paper 1. First, for higher time-frequency resolution, an improved S-transform with new window width adjustment coefficients is introduced. Then use S transform and wavelet transform to extract the features of disturbance signal, and compare the effect of three methods on feature extraction. Finally, the simulation results show that the method can effectively classify the interfering signals, and the energy concentration and resolution are greatly improved.
In order to improve the problem of slow torque and speed response of permanent magnet synchronous motor, a strategy combining model predictive control and particle swarm optimization is proposed in this paper. By analyzing the error of the input parameters in the model predictive control system, the Particle Swarm Optimization (PSO) was introduced to improve the speed loop. When the system model is changed, the particle swarm algorithm is repeatedly iterated to obtain better PI parameters, thereby improving the given current accuracy of the model predictive control system and improving the stability of the system operation. The simulation results show that this method shortens the adjustment time, improves the running stability of the permanent magnet synchronous motor, and reduces the torque ripple problem and the steady-state error of the system.
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