Power Quality Management for Solar-Based Microgrids Using Integrated Artificial Intelligence and Internet of Things Technologies

Design and Performance Analysis of a Fuzzy Logic Controlled Hybrid Wind-Solar-Battery Energy System for Induction Motor Applications

The increasing demand for reliable and high-quality electrical power has accelerated the integration of renewable energy resources into modern power systems. Although wind energy has emerged as a promising renewable source, its intermittent nature introduces significant fluctuations in power generation, affecting system stability and power quality. To overcome these limitations, this paper proposes a hybrid Wind–Solar–Battery Energy System employing a Fuzzy Logic Controller (FLC) for enhanced power management and improved operational performance. The proposed system integrates a photovoltaic (PV) array, a wind energy conversion unit, a battery energy storage system (BESS), power electronic converters, a common DC bus, and a voltage source inverter supplying an induction motor load. Initially, a conventional PI controller is considered for system regulation; however, a fuzzy logic–based control strategy is introduced to improve dynamic response, minimize steady-state error, and reduce Total Harmonic Distortion (THD). The complete hybrid system is modeled and analyzed using MATLAB/Simulink under varying operating conditions. Simulation results demonstrate that the proposed fuzzy logic– controlled hybrid system provides superior power quality, enhanced voltage regulation, effective battery energy management, and reliable power delivery compared with the conventional PI-controlled system. The proposed approach effectively improves renewable energy utilization while ensuring stable operation of induction motor loads under fluctuating environmental conditions.

Comparative Evaluation of Fuzzy and Rule-Based Energy Management Strategies for Photovoltaic–Battery Microgrids

Renewable energy–based microgrids have emerged as an effective solution for integrating distributed energy resources such as photovoltaic (PV) generation and battery energy storage systems (BESS). However, the intermittent nature of renewable energy sources and variations in load demand present significant challenges for maintaining power balance and system stability. This paper presents a comparative evaluation of rule-based and fuzzy logic energy management strategies for a photovoltaic–battery microgrid using a MATLAB/Simulink-based digital twin. The proposed model integrates photovoltaic generation, battery storage, and load demand to simulate realistic operating conditions. Both control strategies are evaluated under varying solar irradiance and load scenarios based on voltage stability, battery state-of-charge dynamics, and energy utilization efficiency. Simulation results indicate that the fuzzy logic controller provides smoother battery operation, improved adaptability, and enhanced system stability compared with the conventional rule-based approach. The proposed framework provides an effective platform for evaluating advanced energy management strategies and supports future implementation in PLC-based microgrid control systems.

Design and Simulation of a Microgrid-Connected Photovoltaic System Using Recurrent Neural Network-Based MPPT Control

Solar PV-Supported Unified Power Quality Conditioner with Mamdani Fuzzy Logic Control

The main reasons for the deterioration in power quality in contemporary distribution systems are the growing share of renewable energy sources and the widespread usage of nonlinear loads. Nonlinear loads like diode rectifiers and power electronic converters produce current harmonics that cause voltage distortion and instability. In addition, the variable output of solar photovoltaic (PV) systems can worsen the fluctuations in the grid. Here, a Mamdani Fuzzy Logic Control (FLC) driven Solar PV-assisted Unified Power Quality Conditioner (UPQC) is proposed to improve power quality and system reliability. The presently recommended method combines a solar panel and a DC-DC boost converter. The P&O MPPT algorithm is used to control the DC link voltage. UPQC utilizes synchronous reference frame (dq0) theory to generate reference signals, and a Mamdani fuzzy controller guarantees adaptive and robust control in different operating conditions. Simulation results verify that there is a substantial improvement in harmonic mitigation, voltage regulation, and DC-link stability compared to the traditional methods.

Grid-Connected Hybrid Renewable Energy System with Fuzzy Logic MPPT for Solar Wind–Battery Integration

This paper presents the development of a multifunctional grid-connected hybrid energy system (HES) integrating solar photovoltaic (PV) modules, a wind energy conversion system (WECS), and battery energy storage. To address the inherent drawbacks of conventional maximum power point tracking (MPPT) algorithms such as delayed response and suboptimal tracking under fast-varying environmental conditions a Mamdani type fuzzy logic-based MPPT controller is proposed. This controller adaptively modulates the duty cycles of individual DC–DC boost converters, enabling optimal power extraction from both PV and wind sources under dynamic operating conditions. The hybrid energy coordination is implemented without requiring linearized system models, thereby increasing control robustness against parameter variations and environmental uncertainties. Grid interfacing is accomplished using a phase-locked loop (PLL) to ensure synchronization in frequency and phase, while an LCL filter is employed at the point of common coupling (PCC) to attenuate high-frequency switching harmonics, thereby improving power quality in accordance with grid codes. The proposed methodology enhances system dynamic response, stabilizes power output, and facilitates seamless energy sharing among multiple sources. Time-domain simulations conducted in MATLAB/Simulink validate the effectiveness of the proposed control scheme, demonstrating improved system stability, reduced harmonic content, and efficient energy regulation. The novelty of this work lies in the adaptive fuzzy logic-driven coordination of hybrid renewable sources in real time, enabling superior performance, high efficiency, and compliance with grid power quality standards in a hybrid energy system environment. Keywords: Fuzzy logic; Hybrid energy; Solar; Wind; Battery storage system.

ANFIS-Controlled Multifunctional Onboard Converter in Grid- and Solar PV-Powered Electric Vehicles

In this paper, a multifunctional onboard converter for integrating solar photovoltaic (PV) power and utility grid for EV charging applications using an ANFIS is presented. Multiple operating modes of converter – such as grid-to-vehicle charging, solar PV charging, propulsion and regenerative braking – are used with a single topology. Proposed intelligent controller based on ANFIS is able to control power flow intelligently by maintaining steady DC-link voltage and adjusting it according to solar irradiances, battery SOC and grid conditions. Compared to traditional control methods, adaptive control strategy offers better transient response, voltage fluctuation, switching stress, and power conversion efficiency. Performance of proposed system is tested using MATLAB/Simulink with different operating conditions. Based on simulation results, converter performs an effective energy management, stable voltage regulation, soft mode switching and reliable charging operation. Solution proposed is an efficient, intelligent and sustainable method for charging on board next generation of electric vehicles using both solar PV and utility grid power sources.