Performance Enhancement of Grid-Connected Hybrid Microgrids Using ANFIS-Controlled Renewable Energy with BESS Integrated UPQC

Abstract

The rapid integration of renewable energy resources into modern distribution networks has significantly improved the sustainability of electrical power systems. However, the intermittent characteristics of photovoltaic (PV) and wind energy conversion systems (WECS), together with the increasing penetration of nonlinear loads, have introduced serious power quality challenges, including harmonic distortion, voltage fluctuations, reactive power imbalance, and poor dynamic stability. This paper proposes an intelligent hybrid microgrid incorporating a Photovoltaic (PV) system, Wind Energy Conversion System (WECS), Battery Energy Storage System (BESS), and Unified Power Quality Conditioner (UPQC) controlled by an Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed ANFIS controller replaces the conventional proportional-integral (PI) controller to achieve faster dynamic response, adaptive reference current generation, and superior DC-link voltage regulation under varying operating conditions. The coordinated operation of PV, WECS, and BESS ensures continuous energy availability while minimizing dependence on the utility grid. The UPQC effectively compensates voltage disturbances, mitigates harmonic currents, and maintains near-unity power factor, thereby improving the overall power quality of the hybrid microgrid. MATLAB/Simulink simulations demonstrate that the proposed ANFIS-based strategy significantly reduces total harmonic distortion (THD), enhances transient performance, improves renewable energy utilization, and increases system reliability compared with the conventional PI-controlled configuration, making it an effective solution for future smart grid applications.

Introduction

This work presents an ANFIS-controlled Unified Power Quality Conditioner (UPQC) integrated with a Photovoltaic (PV) system, Wind Energy Conversion System (WECS), and Battery Energy Storage System (BESS) to improve power quality, renewable energy utilization, and microgrid stability under dynamic operating conditions.

Introduction

The growing demand for electricity, depletion of fossil fuels, and environmental concerns have accelerated the adoption of renewable energy-based microgrids. PV and wind energy are widely used renewable sources but suffer from intermittency due to changing weather conditions, causing voltage fluctuations, frequency deviations, and unstable power generation. In addition, nonlinear loads such as electric vehicle chargers, variable-speed drives, and power electronic converters introduce harmonics that degrade power quality by increasing Total Harmonic Distortion (THD), reducing power factor, and increasing system losses.

To address these challenges, the study employs a Unified Power Quality Conditioner (UPQC), which combines:

• A series converter to compensate voltage disturbances (sag, swell, imbalance, flicker).
• A shunt converter to eliminate current harmonics, compensate reactive power, and maintain unity power factor.

Integrating renewable energy sources and BESS into the UPQC DC-link enhances energy management, stabilizes the DC-link voltage, and improves system reliability. Since conventional PI controllers have limited adaptability under nonlinear conditions, the study proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for intelligent and adaptive control.

Literature Survey

Previous studies have investigated:

• PV-integrated UPQC systems,
• Battery-supported UPQC,
• Advanced converter topologies,
• Intelligent controllers,
• Grid-forming and grid-following strategies,
• AI-based microgrid energy management.

Although these approaches improved voltage regulation and harmonic mitigation, most considered only a single renewable source or focused on specific power quality issues. Very few studies integrated PV, WECS, BESS, and UPQC under a unified ANFIS-based framework. This research addresses that gap by combining multiple renewable sources with adaptive intelligent control.

Methodology

The proposed hybrid microgrid consists of:

• Photovoltaic (PV) system,
• Wind Energy Conversion System (WECS),
• Battery Energy Storage System (BESS),
• Unified Power Quality Conditioner (UPQC),
• Adaptive Neuro-Fuzzy Inference System (ANFIS) controller.

The ANFIS controller continuously monitors the DC-link voltage error and its rate of change:

• Voltage error:

  e(k)=Vdc∗−Vdc(k)e(k)=V_{dc}^*-V_{dc}(k)e(k)=Vdc∗?−Vdc?(k)

• Change in error:

  Δe(k)=e(k)−e(k−1)\Delta e(k)=e(k)-e(k-1)Δe(k)=e(k)−e(k−1)

These inputs are processed using fuzzy inference and neural network learning to generate optimal PWM switching signals for the UPQC converters.

Compared with conventional PI control, the ANFIS controller:

• Automatically adapts to changing operating conditions.
• Regulates the DC-link voltage more accurately.
• Produces better harmonic compensation.
• Improves transient response.
• Reduces overshoot and steady-state error.
• Maintains stable operation during renewable intermittency and sudden load changes.

The coordinated PV–WECS–BESS system stores surplus renewable energy in the battery and supplies power during renewable shortages, ensuring continuous and reliable operation.

Simulation Results

The proposed system was modeled and tested in MATLAB/Simulink under varying operating conditions.

Simulation results show that the ANFIS-controlled UPQC:

• Maintains a stable DC-link voltage.
• Rapidly compensates voltage and current disturbances.
• Protects converter switches during sudden load variations.
• Improves overall power management and system stability.

The harmonic analysis demonstrates excellent power quality:

• Source Voltage THD = 0.15%
• Load Current THD = 3.99%
• Grid Current THD = 1.80%

These values satisfy power quality standards and are significantly better than those achieved using conventional PI controllers.

Conclusion

This research presented an intelligent hybrid microgrid integrating a Photovoltaic (PV) system, Wind Energy Conversion System (WECS), Battery Energy Storage System (BESS), and Unified Power Quality Conditioner (UPQC) controlled by an Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance power quality and system reliability. The proposed approach effectively addresses the challenges associated with renewable energy intermittency, nonlinear loads, and dynamic operating conditions by replacing the conventional PI controller with an adaptive ANFIS controller. The coordinated operation of PV, WECS, and BESS ensures continuous energy availability, efficient power sharing, and stable DC-link voltage regulation under varying environmental conditions. Simulation results obtained using MATLAB/Simulink demonstrate that the proposed controller significantly reduces grid current and load current Total Harmonic Distortion (THD), improves voltage regulation, enhances reactive power compensation, and maintains a near-unity power factor. Compared with conventional control techniques, the ANFIS-based strategy exhibits faster dynamic response, superior disturbance rejection, and greater robustness against system uncertainties. Furthermore, the integration of WECS alongside PV increases renewable energy utilization and reduces dependence on the utility grid, thereby improving the sustainability and operational efficiency of the microgrid. Overall, the proposed ANFIS-controlled PV-WECS-BESS-UPQC system offers an effective and reliable solution for future smart microgrids, providing high-quality electrical power, enhanced renewable energy penetration, and improved stability under diverse operating conditions.

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Copyright

Copyright © 2026 Dr. P. Sangeetha , Kadingu Shivashankar . 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.