PV Battery DC microgrid with ANFIS MPPT in MATLAB
The tutorial initiates with the design of a bi-directional converter, considering a 10x12 output voltage. Parameters like inductor and capacitor values are calculated for this converter to efficiently manage the power flow between batteries and a load.
Battery Modeling for the Converter
Next, the battery model is configured, accounting for a 10x12 lead-acid battery, with the capacity set to 40 Ah and an initial startup charge of 50%. Inductor and capacitor values are derived for the battery and the bi-directional converter to ensure optimal operation.
Load Measurement Using a Multimeter
The usage of a multimeter is explained for measuring load voltage, load current, and power delivered to the load. This multimeter is connected to the load circuit, facilitating measurement of load parameters.
Control Mechanism with a PI Controller
A PI controller is implemented to maintain a load voltage of 220V. This controller generates switching signals for the inverter to regulate the voltage across the load, ensuring it remains constant.
Designing the Solar PV System
A detailed explanation follows on the setup of the solar PV system, including specifications for the series-parallel connections of solar panels, indicating the rating of a single panel at 250W and its maximum voltage and current values.
Determining the L and C Values for the Boost Converter
The transcript delves into the calculation of inductor and capacitor values for the solar PV boost converter, used for voltage regulation. After parameter calculations, the values are incorporated into the boost converter.
Training a Neural Network for PV MPPT Control
The training process of a neural network for maximum power point tracking (MPPT) of a solar PV system is elucidated. Data on irradiation, temperature, and output voltage are used to train the network, enabling it to predict the optimal operating point of the PV system.
Simulating the PV System and Load Management
Using MATLAB tools, the simulated PV system is tested under different irradiation conditions. Load management is discussed, particularly how the battery mode switches between charging and discharging based on the PV system's power generation and load requirements.
Conclusion: Applying MATLAB for Effective Power Management
This demonstration showcases the use of MATLAB for designing battery systems, PV power management, load control, and neural network training for efficient power extraction from solar systems. MATLAB's versatility in simulating power systems is evident in its practical application to design and manage PV systems effectively.
This blog post outlines the step-by-step process explained in the YouTube transcript for designing a battery, configuring a bi-directional converter, and simulating a solar PV system using MATLAB tools. The content sheds light on the significance of MATLAB in the field of power systems and its applications in effectively managing power resources.
Comments