Performance Analysis of an Artificial Neural Network-Based Maximum Power Point Tracking Algorithm for Solar Photovoltaic Systems
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- 13 hours ago
- 4 min read
Abstract
The inherent non-linearity of solar photovoltaic (PV) current–voltage (I–V) and power–voltage (P–V) characteristics poses a significant challenge for efficient energy harvesting under fluctuating environmental conditions. In this study, an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller is proposed and implemented to optimize the output of a 250 W PV system. The methodology utilizes a three-layer feed-forward neural network developed within the MATLAB/Simulink environment. The network is trained using the Levenberg–Marquardt algorithm, with solar irradiation and ambient temperature serving as the primary input vectors to predict the optimal operating voltage . Simulation results indicate that the ANN-based controller achieves a regression coefficient , demonstrating an exceptional correlation between predicted and theoretical maximum power points. The system's performance was rigorously evaluated under rapid atmospheric transients and varying electrical loads. It is observed that the proposed algorithm maintains high tracking efficiency and superior dynamic stability, effectively eliminating the steady-state oscillations associated with conventional MPPT techniques.
Keywords:
Photovoltaic Systems, MPPT, Artificial Neural Network, MATLAB/Simulink, Power Electronics.
I. Introduction
The global imperatives of decarbonization and energy security have positioned Solar Photovoltaic (PV) technology as a fundamental pillar of the renewable energy transition. However, the conversion efficiency of PV modules remains constrained by the non-linear relationship between current, voltage, and environmental factors. Specifically, the output power is a complex function of solar irradiation and temperature, characterized by a unique Maximum Power Point (MPP) that shifts dynamically. To mitigate these losses and maximize energy yield, the integration of high-performance Maximum Power Point Tracking (MPPT) controllers is critical.
Traditional MPPT methods, notably the Perturb and Observe (P&O) and Incremental Conductance algorithms, are widely utilized due to their simplicity. Nevertheless, these deterministic approaches often exhibit oscillations around the MPP during steady-state operation and fail to track the peak rapidly during fast atmospheric transients. Consequently, a strategic shift toward Artificial Intelligence (AI) techniques is observed in contemporary research. Neural network architectures provide a robust framework for handling non-linear mapping without the need for complex mathematical approximations of the physical environment during real-time execution.
The objective of this research is the design and implementation of an ANN-based MPPT controller that utilizes irradiation and temperature as inputs to predict the target voltage .
II. System Configuration and Mathematical Modeling
A 250 W solar PV module interfaced with a DC–DC boost converter is considered. The boost converter acts as the control actuator for MPPT regulation.
Table 1
Technical Specifications of the 250 W Solar PV Module (STC: 1000 W/m², 25°C)
Parameter | Symbol | Value |
Maximum Power | 250 W | |
Short Circuit Current | 8.66 A | |
Current at MPP | 8.15 A | |
Open Circuit Voltage | 37.3 V | |
Voltage at MPP | 30.7 V |
Governing Equations
The maximum power point current and voltage under varying environmental conditions are expressed as:
Where:
· = Solar irradiation (W/m²)
· = Cell temperature (°C)
· = Current temperature coefficient
· = Voltage temperature coefficient
·
·
These equations serve as the ground truth dataset for ANN training.
III. Proposed ANN-Based MPPT Control Strategy
Neural Network Architecture
The proposed feed-forward backpropagation ANN consists of:
1. Input Layer:
o Irradiation
o Temperature
2. Hidden Layer:
o 10 neurons
o Bias terms included
o Non-linear activation function (tansig)
3. Output Layer:
o Single neuron
o Outputs predicted reference voltage
Training Algorithm
The network is trained using the Levenberg–Marquardt (LM) optimization algorithm.
The weight update rule is given by:
Where:
· = Jacobian matrix
· = Damping factor
· = Error vector
· = Identity matrix
Performance Metrics
Mean Square Error (MSE):
Regression coefficient:
This indicates perfect mapping between predicted and theoretical .
Control Loop Integration
The voltage error is computed as:
The PID control law is expressed as:
Where:
· = Duty cycle
· = PID gains
The duty cycle regulates the boost converter:
IV. MATLAB/Simulink Implementation
The system model consists of:
· PV Array Block
· ANN Controller Block
· PID Controller
· DC–DC Boost Converter
· Variable Load
Irradiation steps applied at 2-second intervals:
1000 → 800 → 600 → 400 W/m²
Load variation:
20Ω → 30Ω → 40Ω
V. Results and Discussion
Case Study 1: Variable Irradiation
Irradiation (W/m²) | Output Power (W) |
1000 | 250.2 |
800 | 199.9 |
600 | 149.6 |
400 | 98.97 |
The ANN successfully tracked theoretical maximum power with negligible overshoot.
Case Study 2: Variable Load
Load changes from 20Ω to 40Ω did not affect the MPP tracking capability. The duty cycle dynamically adjusted to maintain:
Comparative Observation
· No steady-state oscillations
· Faster convergence than P&O
· Smooth power response
· Improved dynamic stability
VI. Conclusion and Future Scope
This study demonstrates that the ANN-based MPPT controller significantly enhances PV system performance. The perfect regression confirms accurate environmental mapping.
Critical Technical Takeaways
1. Precision Tracking: Accurate 250 W tracking with zero oscillation.
2. Dynamic Robustness: Stable performance under rapid irradiation and load variation.
3. Computational Efficiency: 10-neuron architecture balances accuracy and processing speed.
Future Scope
· Hardware-in-the-Loop (HIL) validation
· Embedded DSP/FPGA implementation
· Hybrid ANN–PSO optimization
· ANN–GA weight tuning
· Partial shading condition analysis
VII. YouTube Video
VIII. Purchase link of the Model
SKU: 0013
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