Introduction to the Simulation Model
In this study, we examine a simulation model of a grid-connected PV system controlled by PSO-tuned ANFIS for MPPT. The system consists of several key components: the PV array, an inverter, a coupling inductor, the grid, and a local load. The inverter is responsible for converting the DC power from the PV panels into AC power for the grid, and this process is controlled using a sophisticated MPPT technique.
Components of the PV System
The PV array used in the system is configured with 25 panels in series, grouped into 18 strings, providing a maximum power output of around 96 kW. This configuration is connected to a grid via a single-stage inverter, where the output is carefully controlled using voltage and current regulation. The primary aim is to maintain the voltage across the system in such a way that the maximum power can be extracted from the PV panels, even as environmental conditions change.
MPPT Control Strategy: PSO-Tuned ANFIS
The key to optimizing the power output of the PV system lies in the MPPT technique. In this model, a PSO-tuned ANFIS is used to track the maximum power point. The system takes two main input parameters—solar radiation and temperature—and uses them to determine a reference voltage for the system. This reference voltage is then compared to the actual voltage, and adjustments are made to ensure the voltage stays at the optimal point for maximum power extraction.
The PSO algorithm is used to train the ANFIS, improving the accuracy and efficiency of the MPPT process. This training allows the system to adapt to changing environmental conditions and ensure the PV array is operating at its maximum potential.
Power Flow Management: Source, Load, and Grid
A crucial aspect of the system is managing the flow of power between the PV array, the local load, and the grid. The load current is measured, and based on this, the system calculates the required source current. This is done by measuring the voltage and current of the PV system, determining the power output, and comparing it to the load power requirements.
The excess power, if any, is then sent to the grid. This ensures that any surplus energy generated by the PV array is utilized efficiently, either by supplying the local load or by feeding it back into the grid.
Inverter Control and Grid Integration
The inverter plays a central role in controlling the power flow from the PV system to the grid. The reference current required for grid connection is calculated and compared to the actual inverter current. This difference, or error current, is then processed by a controller, which generates pulse signals to control the operation of the inverter.
This control mechanism ensures that the inverter operates in sync with the grid, allowing for a stable power supply. The system uses six IGBTs (Insulated Gate Bipolar Transistors) for precise control of the inverter.
PSO Training for ANFIS
The Particle Swarm Optimization (PSO) algorithm is applied to train the ANFIS. Over the course of several iterations, the PSO algorithm fine-tunes the parameters of the ANFIS, ensuring it accurately tracks the maximum power point. The training data, including the error values and performance metrics, are used to refine the system's ability to extract maximum power from the PV panels.
Once the training is complete, the optimized ANFIS can be used in real-time operations to manage the MPPT process effectively.
Simulation Results and Performance Analysis
The simulation results demonstrate the effectiveness of the PSO-tuned ANFIS MPPT technique. Under optimal conditions with solar radiation of 1,000 W/m², the system generates around 95.8 kW of power, with the PV voltage maintained at approximately 725 V. When radiation drops to 500 W/m², the power output decreases to around 48.5 kW, which reflects the reduced energy available from the PV panels due to lower sunlight intensity.
The inverter's output current adjusts accordingly, peaking at around 200 A during full sunlight and reducing to about 100 A when radiation is halved. The grid current mirrors these changes, with excess power being sent to the grid during high radiation, and no excess being available when the radiation decreases.
Conclusion: Efficient Power Extraction and Grid Integration
This simulation model demonstrates how a PSO-tuned ANFIS-based MPPT can efficiently control a grid-connected PV system. By adapting to changes in solar radiation and temperature, the system ensures that the maximum power is always extracted from the PV panels. Moreover, excess power is effectively fed into the grid, making the system both reliable and beneficial for energy distribution.
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