Hybrid Neural Network PO MPPT for Solar PV System

Hybrid Neural Network PO MPPT for Solar PV System

System Overview

The solar PV system under consideration features a 250-watt PV panel with the following specifications:

• Open circuit voltage: 37.3 volts

• Voltage at maximum power point: 30.7 volts

• Short circuit current: 8.6 amps

• Current at maximum power point: 8.15 amps
  

PV Panel Characteristics

• Maximum power output varies with irradiance:

• 1000 W/m²: 250 watts

• 800 W/m²: 199.9 watts

• 600 W/m²: 149.6 watts

• 400 W/m²: 98.97 watts

• 200 W/m²: 48.37 watts

Hybrid Neural Network PO MPPT System Design

The hybrid system combines a Neural Network (NN) MPPT algorithm with the traditional P&O MPPT technique to optimize power extraction from the PV panel.

1. Components

• Boost Converter: Positioned between the PV panel and the load, regulates the power flow.

• Neural Network MPPT: Utilizes neural network models to predict and adjust the maximum power point based on irradiance and temperature inputs.

• P&O MPPT: Perturbs the operating point to track changes in PV panel power output, adjusting the boost converter's duty cycle accordingly.

1. Operational Flow

• Neural Network MPPT: Predicts the optimal voltage for maximum power output based on real-time environmental conditions.

• P&O MPPT: Adjusts the duty cycle based on changes in PV panel voltage and power, aiming to track the maximum power point.

Integration and Control Logic

The hybrid system integrates both MPPT algorithms using a control logic that selects between them based on real-time conditions:

• Selection Switch: Enables switching between neural network and P&O MPPT modes.

• Duty Cycle Adjustment: Neural network and P&O outputs are combined using an averaging mechanism to optimize the boost converter's duty cycle.

• Manual Switch: Allows manual selection of MPPT modes based on environmental conditions for optimal performance.

Simulation and Comparative Analysis

Performance under Irradiance Variations:

1. Simulation Setup: Irradiance levels vary from 1000 W/m² to 200 W/m² every 0.2 seconds to simulate changing environmental conditions.

2. Hybrid NN PO MPPT Results:

• Efficiently reaches and maintains the maximum power point under varying irradiance levels.

• Shows reduced oscillations and quicker response compared to individual MPPT techniques.

1. Comparison with Individual MPPT Techniques:

• P&O MPPT: Demonstrates steady-state performance with occasional oscillations.

• NN MPPT: Provides accurate tracking but with slower response times during dynamic changes.

• Hybrid System: Combines the advantages of both techniques, minimizing oscillations and achieving rapid response to irradiance variations.

Conclusion

The Hybrid Neural Network PO MPPT system represents a significant advancement in solar PV technology, leveraging neural network predictive capabilities with robust MPPT techniques. By integrating these approaches, the system optimizes energy harvesting efficiency across varying environmental conditions.