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100 kW Grid-connected PV System with Fuzzy and P&O MPPT

100 kW Grid-connected PV System with Fuzzy and P&O MPPT

In this simulation model developed in MATLAB, we explore a grid-tied PV battery system incorporating a C converter. The system consists of PV panels, a battery, an inverter, a filter, and a connection to the grid. The C converter facilitates efficient power conversion between the PV panels and the battery, ensuring optimal energy utilization and grid interaction.

PV Panel Details:

  • The PV array comprises single panels with a rating of 23.15 watts each.

  • Key specifications include open-circuit voltage, short-circuit current, voltage at maximum power point, and current at maximum power point.

  • The array, consisting of 8 panels in a series, can generate a maximum power of approximately 17,705 watts under standard irradiance conditions.

System Configuration:

  • The PV array is connected to the DC bus via the C converter, which regulates the power flow.

  • The battery, comprising two 36V 12Ah batteries with a capacity of 40Ah, is directly connected to the DC bus.

  • The inverter, controlled by MPPT algorithms, converts DC power from the battery into AC power for grid interaction.

  • Energy management logic, including neural network-based control, optimizes power flow between the PV array, battery, and grid.

Simulation and Analysis:

  1. PV Power Generation:

  • Simulated PV power generation is observed based on varying irradiation levels, with instantaneous power output reaching up to 850 watts.

  • Power generation data is analyzed to assess the system's ability to track maximum power points and adapt to changing environmental conditions.

  1. Battery Charging and Grid Interaction:

  • The battery's state of charge is monitored as it charges from the PV array and discharges to the grid.

  • In scenarios of low PV power generation, power is drawn from the grid to supplement battery charging, ensuring continuous energy supply.

  1. Inverter Control and Grid Connectivity:

  • Inverter operations are governed by control algorithms based on reference currents derived from energy management logic.

  • The power exchange between the battery, inverter, and grid is analyzed, showcasing bidirectional power flow and grid synchronization.

  1. Neural Network Energy Management:

  • The neural network model, trained with simulated data, governs energy flow decisions based on real-time PV and battery parameters.

  • By dynamically adjusting reference currents, the system optimizes power utilization and grid interaction, ensuring efficient energy management.

Conclusion: The simulation results demonstrate the effective operation of the grid-tied PV battery system with a C converter in MATLAB. Through sophisticated control algorithms and neural network-based energy management, the system efficiently harnesses solar energy, stores excess power in the battery, and interacts with the grid as needed. This holistic approach ensures a reliable and sustainable energy supply while maximizing system efficiency.

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