研究目的
To design an adaptive fuzzy sliding mode command-filtered backstepping (AFSCB) controller for an islanded PV microgrid with an energy storage system to control the DC bus voltage and output current/power of the PV system, addressing uncertainties in circuit parameters and unmodeled dynamics.
研究成果
The AFSCB controller effectively controls the DC bus voltage and output power of the PV system in an islanded microgrid, demonstrating improved dynamic characteristics, reduced chattering, and higher power quality compared to PI and SCB controllers. It handles parameter uncertainties and unmodeled dynamics through adaptive estimation and fuzzy approximation, ensuring system stability and performance under varying irradiance, temperature, and load conditions. Future work should focus on experimental validation to further verify effectiveness.
研究不足
The study is based on simulation in MATLAB/Simulink, not real-world hardware implementation, which may not capture all practical uncertainties and disturbances. The controller design assumes specific system parameters and may require tuning for different setups. The fuzzy system and adaptive laws add complexity, potentially increasing computational demands in real-time applications.
1：Experimental Design and Method Selection:
The study uses a simulation-based approach in MATLAB/Simulink to model and control a 100 kW islanded PV microgrid with an energy storage system. The AFSCB controller integrates command-filtered backstepping, sliding mode control, adaptive parameter estimation, and fuzzy logic to handle uncertainties and unmodeled parts. Lyapunov stability theory is employed for stability proof.
2：Sample Selection and Data Sources:
A simulated PV array and energy storage system are used, with parameters such as irradiance and temperature varied over time to test controller performance under different conditions.
3：List of Experimental Equipment and Materials:
The simulation involves components like PV panels, DC/DC converters, voltage source converters (VSCs), LC filters, transformers, and batteries, modeled in MATLAB/Simulink with specific parameters (e.g., inductances, capacitances, resistances).
4：Experimental Procedures and Operational Workflow:
The simulation runs for 2 seconds, with controller intervention starting at 0.05s. MPPT control begins at 0.4s, and load changes are introduced at 1.8s to observe dynamic responses. The controller processes signals in dq-frame, uses command filters to reduce computational complexity, and applies adaptive laws and fuzzy approximation in real-time.
5：05s. MPPT control begins at 4s, and load changes are introduced at 8s to observe dynamic responses. The controller processes signals in dq-frame, uses command filters to reduce computational complexity, and applies adaptive laws and fuzzy approximation in real-time.
Data Analysis Methods:
5. Data Analysis Methods: Performance is evaluated by comparing output power, DC bus voltage tracking, total harmonic distortion (THD), and state of charge (SOC) under different controllers (PI, SCB, AFSCB) using MATLAB/Simulink tools for waveform analysis and stability assessment.
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