Development and Performance Analysis of an Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Maximum Power Point Tracking for Solar Photovoltaic Applications
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Abstract
Efficient Maximum Power Point Tracking (MPPT) remains a critical challenge in solar photovoltaic (PV) systems due to the non-linear output characteristics of solar cells under fluctuating environmental conditions. This paper presents the development and validation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller designed for a standalone 250 W PV system. By integrating the learning capabilities of neural networks with the linguistic transparency of fuzzy logic, the proposed Sugeno-type ANFIS model maps environmental variables—Irradiation (G) and Temperature (T)—directly to the optimal reference voltage . The system was modeled and simulated within the MATLAB/Simulink environment using a DC-DC boost converter topology. A hybrid learning algorithm was employed to train the network using 1,000 data points, achieving a minimum training error of approximately . Performance results demonstrate that the ANFIS controller provides high precision in power extraction, maintaining optimal output despite rapid step changes in irradiation and sudden variations in load resistance. Furthermore, the proposed architecture effectively reduces hardware complexity by eliminating the need for a PV current sensor in the reference voltage generation stage. The findings confirm that the ANFIS approach offers a robust, highly responsive, and cost-effective solution for modern solar energy applications.
Keywords — ANFIS, Boost Converter, Hybrid Learning Algorithm, MPPT, Photovoltaic Systems.
I. Introduction
The strategic global transition toward renewable energy has positioned solar photovoltaic (PV) systems as a cornerstone of sustainable power generation. However, the conversion efficiency of PV systems is inherently limited by the non-linear electrical characteristics of solar cells, which exhibit a unique Maximum Power Point (MPP) that shifts according to atmospheric conditions. To maximize energy harvesting, it is technically necessary to implement Maximum Power Point Tracking (MPPT) to ensure the PV array operates at its peak capacity regardless of environmental stochasticity.
Traditional MPPT methods, such as Perturb and Observe (P&O) or Incremental Conductance, often struggle with accuracy and oscillation under rapidly changing conditions. To mitigate these limitations, this study introduces the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a superior hybrid control approach. ANFIS combines the adaptive learning of artificial neural networks with the decision-making logic of fuzzy systems, allowing the controller to mitigate stochastic fluctuations with higher precision than conventional algorithms. The primary objective is to implement and validate an ANFIS-based controller that maps irradiation and temperature directly to the optimal operating voltage . This strategy provides a significant strategic benefit: it reduces the hardware sensor count, specifically eliminating the requirement for a PV current sensor during the reference generation phase, thereby optimizing system cost and reliability.
The subsequent sections detail the system modeling, the architectural specifics of the Sugeno-type ANFIS controller, the simulation framework, and a comprehensive analysis of the system's robustness under dynamic disturbances.
II. System Configuration and Modeling
The effectiveness of an MPPT algorithm is intrinsically linked to the hardware topology it governs. This study utilizes a standalone configuration where a 250 W PV source is interfaced with a DC-DC boost converter to drive a variable load.
A. PV Module Mathematical Basis
The system utilizes a 250 W PV module. The electrical behavior of the PV cell is represented by the standard single-diode mathematical model, where the output current is defined as:
where:
· = Photo-generated current
· = Dark saturation current
· = Series resistance
· = Parallel resistance
· = Thermal voltage
· = Diode ideality factor
The model incorporates the short-circuit current , open-circuit voltage , and temperature coefficients for current ( ) and voltage ( ) to ensure high-fidelity simulation across varying Irradiation (G) and Temperature (T).
B. DC-DC Boost Converter and Impedance Matching
The boost converter serves as the critical actuator for MPPT. By adjusting the duty cycle , the controller performs impedance matching. The relationship between the input resistance seen by the PV array and the output load resistance is governed by:
By dynamically modulating , the ANFIS controller ensures that matches the optimal resistance of the PV module at the MPP, thereby facilitating maximum power transfer according to the Maximum Power Transfer Theorem.
III. ANFIS-Based MPPT Control Strategy
The proposed controller utilizes a Sugeno-type inference system to handle the non-linearities of the PV output by learning the direct mapping between environmental inputs and the optimal operating point.
A. ANFIS Architecture
The ANFIS model is structured into five distinct functional layers:
1. Layer 1 (Fuzzification): Receives Irradiation (G) and Temperature (T). Input values are converted into fuzzy sets using three triangular membership functions per input.
2. Layer 2 (Rule Base): Executes the fuzzy “AND” operation to determine the firing strength of each predefined rule.
3. Layer 3 (Normalization): Calculates the ratio of each rule’s firing strength to the sum of all firing strengths.
4. Layer 4 (Defuzzification): Applies Sugeno-type linear functions to the normalized firing strengths.
5. Layer 5 (Summation): Computes the final crisp output as the weighted average of the individual rule outputs, yielding the optimal reference voltage .
B. Data Collection and Training Phase
A dataset of 1,000 random data points was generated within specific environmental bounds:
· Temperature: 15°C to 35°C
· Irradiation: 0 to 1000 W/m²
This data provides the input-output pairs necessary for the network to characterize the PV module's peak performance curve.
C. Hybrid Learning Algorithm
The model was optimized using a Hybrid Learning Algorithm, which employs:
· Back-propagation for membership function parameters
· Least-squares estimation for the Sugeno linear parameters
Using a grid partition method and 100 training epochs, the system achieved a Root Mean Square Error (RMSE) of:
This exceptionally low error indicates high-fidelity mapping and suggests that the Sugeno system has successfully captured the underlying physics of the PV module without significant overfitting.
D. Control Loop Integration
The ANFIS block functions as the reference generator, outputting based on sensed and . This is compared against the actual measured PV voltage. The error is processed by a Proportional-Integral (PI) controller to generate the duty cycle for the PWM generator, which subsequently drives the boost converter's IGBT. This configuration strategically reduces hardware complexity by eliminating the PV current sensor in the reference voltage stage.
IV. Simulation Model and Parameters
The system was validated using MATLAB/Simulink to ensure high-fidelity modeling of the power electronic transitions.
Table I
PV System and Simulation Parameters
Parameter | Value |
Maximum Power | 250 W |
Standard Test Conditions (STC) | 1000 W/m², 25°C |
Inference System Type | Sugeno-type |
Membership Function (MF) Type | Triangular |
Number of MFs per Input | 3 |
Training Iterations (Epochs) | 100 |
Training RMSE | |
Temperature Range | 15°C – 35°C |
Irradiation Range | 0 – 1000 W/m² |
V. Results and Discussion
The effectiveness of the ANFIS controller was analyzed through step-response tests involving irradiation and load disturbances.
A. Case 1: Response to Varying Irradiation
Irradiation was decreased every 2 seconds from 1000 W/m² to 800, 600, 400, and 200 W/m² at a constant 25°C.
· At 1000 W/m², the controller extracted approximately 250 W.
· At 800 W/m², power adjusted to approximately 200 W.
· At 600 W/m², the system tracked accurately to approximately 150 W.
The ANFIS controller demonstrated rapid convergence, maintaining a duty cycle near 0.63 at lower irradiation levels to sustain tracking accuracy.
B. Case 2: Response to Sudden Load Changes
With irradiation fixed at 1000 W/m², the load resistance was increased cumulatively:
1. 0.0 s – 0.3 s: Initial load of 20 Ω. Duty cycle at approximately 0.55.
2. 0.3 s – 0.6 s: Load increased to 50 Ω (30 Ω added). Duty cycle shifted to 0.58.
3. 0.6 s onwards: Load increased to 90 Ω (additional 40 Ω added). Duty cycle shifted to 0.63.
Despite these significant disturbances, the ANFIS controller maintained at approximately 250 W, demonstrating superior robustness through dynamic duty cycle modulation.
VI. Conclusion
This study successfully demonstrated an ANFIS-based MPPT controller that provides high precision and rapid tracking for solar PV applications. By utilizing a Sugeno-type inference system, the model achieved a near-negligible training RMSE ( ), ensuring high-fidelity mapping of the maximum power point. The controller proved robust against both rapid environmental changes and sudden load variations, successfully modulating the duty cycle from 0.55 to 0.63 to maintain peak power. A key strategic advantage of this implementation is the reduction of hardware complexity through the elimination of the PV current sensor for reference generation. Future work will focus on hardware-in-the-loop (HIL) validation and the integration of metaheuristic algorithms to further optimize the membership function parameters for extreme climatic conditions.
VII. YouTube Video
VIII. Purchase link of the Model
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