Adaptive Dual-Port Photovoltaic Grid-Connected Inverter with Intelligent Voltage Regulation under Solar Irradiance Disparity Using ANFIS-Based Energy Management

Abstract

The increasing penetration of renewable energy resources into modern power systems has intensified the need for intelligent photovoltaic (PV) conversion systems capable of delivering reliable, efficient, and high-quality electrical power under continuously varying environmental conditions. This paper proposes an adaptive dual-port grid-connected photovoltaic inverter incorporating an Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent voltage regulation and enhanced energy management during solar irradiance disparity. The proposed architecture employs two independent photovoltaic sources connected through a dual-port converter that effectively balances power flow under both uniform and non-uniform irradiance conditions. The intelligent ANFIS controller dynamically regulates the converter switching operation to stabilize the DC-link voltage, improve transient response, and minimize harmonic distortion under rapidly changing atmospheric conditions. Four irradiance scenarios are investigated to evaluate the robustness of the proposed system, including complete shading of one photovoltaic module. MATLAB/Simulink simulations demonstrate that the proposed control strategy successfully maintains a constant output voltage despite significant variations in photovoltaic generation. Furthermore, the proposed system achieves superior grid synchronization, lower Total Harmonic Distortion (THD), enhanced voltage stability, improved converter efficiency, and reliable energy transfer compared with conventional control methods. The proposed intelligent dual-port photovoltaic architecture offers a practical solution for future smart-grid applications requiring high reliability, superior power quality, and sustainable renewable energy integration.

Introduction

This paper proposes an Adaptive Dual-Port Grid-Connected Photovoltaic (PV) Inverter using an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to improve voltage regulation, power quality, and grid integration under varying solar irradiance conditions.

Introduction

The growing demand for clean energy and the depletion of fossil fuels have accelerated the adoption of solar photovoltaic systems. Although PV technology is environmentally friendly and cost-effective, its performance is highly affected by changing environmental conditions such as irradiance, temperature, cloud cover, and partial shading. These variations cause power fluctuations, voltage instability, increased converter stress, and poor power quality. Conventional single-input PV systems and fixed-parameter controllers struggle to maintain stable operation under these nonlinear conditions.

Research Motivation

Large-scale PV installations often experience irradiance mismatch among modules, reducing energy extraction efficiency and causing DC-link voltage oscillations and higher harmonic distortion. Dual-port converter architectures address this issue by independently processing multiple PV sources while maintaining a common DC-link voltage. However, traditional controllers such as PI, PR, and Fuzzy Logic Controllers have limited adaptability to rapid environmental changes, motivating the use of intelligent control techniques like ANFIS.

Literature Survey

Previous studies have demonstrated improvements in PV systems through advanced converter topologies, PWM techniques, transformerless inverters, MPPT methods, battery energy storage, and AI-based controllers. ANFIS has shown superior transient response, voltage regulation, and harmonic suppression compared to conventional controllers. Despite these advances, few studies combine dual-port PV architectures with ANFIS-based adaptive voltage regulation under irradiance disparity while maintaining grid power quality, creating a clear research gap addressed by this work.

Proposed Methodology

The proposed system consists of:

• Two independent PV arrays connected through separate DC–DC boost converters.
• Independent MPPT algorithms for each PV array.
• A common DC-link capacitor.
• A three-phase Voltage Source Inverter (VSI) for grid connection.
• An ANFIS controller that regulates the DC-link voltage using voltage error and change in error as inputs.

The system is modeled in MATLAB/Simulink and evaluated under four operating scenarios:

1. Uniform irradiance,
2. Moderate irradiance mismatch,
3. Severe partial shading,
4. Complete irradiance loss in one PV array.

Compared with conventional PI control, the ANFIS controller provides:

• Faster transient response,
• Reduced DC-link voltage ripple,
• Better voltage regulation,
• Lower harmonic distortion,
• Improved converter efficiency,
• Enhanced grid synchronization,
• Greater robustness against nonlinear operating conditions.

Simulation Results

Simulation results compare the proposed ANFIS controller with a conventional PI controller.

For the PI-controlled system:

• The three-phase output voltage remains sinusoidal but exhibits noticeable voltage ripple and distortion during irradiance changes.
• The output current shows oscillations due to slower controller response.
• FFT analysis indicates poor power quality with:
  • Grid Voltage THD = 5.71%
  • Grid Current THD = 17.98%

These results demonstrate that conventional PI control is insufficient for maintaining high power quality under dynamic solar conditions. The proposed ANFIS-based dual-port system is designed to overcome these limitations by providing adaptive control, improved voltage stability, reduced THD, higher efficiency, and more reliable renewable energy integration for future smart-grid applications.

Conclusion

This study proposed an adaptive dual-port photovoltaic (PV) grid-connected inverter integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve voltage regulation, power quality, and overall system performance under varying solar irradiance conditions. The dual-port architecture enabled independent power processing from two photovoltaic sources, while the ANFIS controller intelligently regulated converter operation to maintain a stable DC-link voltage and constant output voltage during irradiance mismatch. MATLAB/Simulink simulation results under four operating scenarios demonstrated that the proposed system achieved faster transient response, enhanced voltage stability, reduced Total Harmonic Distortion (THD), and improved grid synchronization compared with the conventional PI-controlled approach. Comparative analysis further confirmed superior converter efficiency, lower voltage ripple, improved harmonic suppression, and greater robustness under dynamic environmental conditions. Overall, the proposed intelligent control strategy offers an efficient and reliable solution for modern grid-connected photovoltaic systems, supporting improved renewable energy utilization and providing a strong foundation for future smart-grid applications and advanced distributed energy systems.

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Copyright

Copyright © 2026 C. Radhacharan , Duvvala Bharath Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.