研究目的
To propose an adaptive radial basis function (RBF) neural network (NN) fuzzy control scheme to enhance the performance of shunt active power ?lter (APF).
研究成果
An adaptive RBF NN fuzzy controller is successfully applied in three-phase shunt APF. The simulation results confirm the effectiveness of the proposed controller and illustrate that the APF system based on the proposed method has the outstanding compensation performance and strong robustness.
研究不足
The technical and application constraints of the experiments include the need for careful selection of controller parameters to achieve the goal that compensation current can track instruction current. Potential areas for optimization include further reducing the chattering effect and improving the robustness of the system under varying nonlinear loads.
1：Experimental Design and Method Selection:
The RBF NN is utilized on the approximation of nonlinear function in the APF dynamic model and the weights of the RBF NN are adjusted online according to adaptive law from the Lyapunov stability analysis. The sliding mode control term is adjusted by adaptive fuzzy systems to compensate the network approximation error and eliminate the existing chattering.
2：Sample Selection and Data Sources:
The simulation results of APF using the proposed method are used to confirm the effectiveness of the proposed controller.
3：List of Experimental Equipment and Materials:
Matlab/Simulink package with SimPower Toolbox is used for simulation.
4：Experimental Procedures and Operational Workflow:
The proposed control strategy is applied to a three-phase shunt APF system with parameters shown in Table
5：Data Analysis Methods:
The performance of the proposed controller is evaluated through simulation results, including grid current waveform and harmonic spectrum analysis.
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