Design and Simulation of a Micro-Inverter for Microgrid Applications Using a Recurrent Neural Network-Based Control Strategy

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

Simulation and Modeling of a Photovoltaic Energy System Using Boost Converter and MPPT Control to Improve Solar Power Extraction

In this paper, a photovoltaic (PV) system with a dual-phase boost converter and maximum power point tracking (MPPT) control has been designed, simulated, and experimentally validated. The suggested converter uses coupled inductors and active clamp connection, which lightens current stress on the power switches, minimizes input current ripple, and prevents voltage spikes. This enables low voltage rated IGBT to be used, reducing conduction loss and the cost of the system. Leakage inductances are used to recover energy, and it is increasing efficiency in general. This system was simulated in MATLAB/Simulink, and a prototype created and tested under natural conditions of a real solar irradiance in Kirkuk, Iraq. The results indicate that the system achieves a peak efficiency of 94.6% under maximum irradiance (920 W/m² at 1:00 PM local time), with the MPPT controller successfully tracking the maximum power point during changing environmental conditions. An extensive ablation experiment determines the individual contribution of the dual-phase topology (2.8% efficiency enhancement) and active clamp circuit (2.5% efficiency enhancement and 28.4% voltage stress reduction). The comparative analysis of the results with the literature of the recent past reveals that the design being proposed can provide the competitive performance (94.6% peak efficiency, <5% ripple) at the lower complexity and cost compared to the sophisticated ANN-based techniques. The simulation results showed a strong correlation with experimental measurements, with a maximum deviation not exceeding 0.6% (a simulated efficiency of 94.6% versus an experimental efficiency of 94.0%), thereby confirming the validity of the proposed design approach. The statistical validation of the results in several trials indicates a consistent performance with a standard deviation of less than 0.4 percent. The system exhibits quick dynamic response to the changes in irradiance which stabilize in 150-200 milliseconds. This piece of work offers a low cost and efficient method of extracting solar energy in areas where the solar potential is high and verified using real environment data of Kirkuk, Iraq. The results will be used to design viable, commercializable PV systems with balanced performance, ease and economic viability to be deployed in large scale installations in other related climatic areas.

PV-Fed Electric Vehicle Drive Architecture with Advanced X-Boost Converter and Chaotic Optimized Control

The progression in Electric Vehicles (EVs) requires effective, reliable and maintainable power conversion system which is incorporated with Renewable Energy Sources (RESs) for eco-friendly charging. This research presents a hybrid energy system, with PV as primary source for energizing the Brushless DC motor (BLDC) of EV. The intermittent nature results with lower and fluctuated DC output boosted using a novel Extreme gain X-Boost converter. The converter shows higher power conversion with reduced stress and current ripples. The regulation of converter is performed using Proportional Integral (PI) controller with its parameters tuned using Chaotic Parrot Optimization Algorithm (CPOA), providing minimized overshoot, improved steady state error, faster convergences and enhanced settling time. In addition, battery and grid system is incorporated to power the BLDC motor at times of energy scarcity. The developed system is examined through MATLAB simulation under varying operating conditions of PV. The outcomes demonstrates improved converter efficiency of 96.16%, with higher voltage. Moreover, the controller offers reduced settling time of 0.3s with minimized overshoot. In addition, the Total Harmonic Distortion (THD) of the system lies below 5% indicating effective power quality based on IEEE standard limits. This shows that the proposed system offers suitability for EV charging with effective energy management.

Design and Implementation of a PFC-Assisted MPPT Controller with DC–DC Converter for Grid Connected Photovoltaic Systems

The traditional photovoltaic (PV) systems have several limitations, such as low voltage gain and inefficient power extraction, this study proposes a single-phase grid-connected PV system to increase solar power conversion and grid integration efficiency. A DC–DC NX cascaded converter is used by the system to boost the PV output voltage, and a Radial Basis Function Neural Network (RBFNN)-based MPPT (Maximum Power Point Tracking) is used to maximise the PV panel’s power output. PWM signals are generated by a DSPIC30F4011 controller to regulate the inverter and converter stages. Furthermore, a DQ control theory-based Proportional Integral (PI) controller controls the inverter output and keeps it in sync with the utility grid. The proposed system achieves a converter efficiency of 93% and an MPPT tracking efficiency of 95%, according to the simulation results.

Performance analysis of a solar PV and battery based bipolar microgrid system

This paper presents a comprehensive performance analysis of a solar photovoltaic (PV) and battery-based bipolar microgrid system under various conditions. The system is designed to enhance reliability, efficiency, and power quality in modern energy networks. The proposed system consists of a PV system with a boost converter, a battery system with a bidirectional DC–DC converter, and a three phase neutral point clamped inverter. The system was modeled and simulated in MATLAB/Simulink software to evaluate the system under multiple operating scenarios: (i) constant solar irradiance with varying load conditions, (ii) varying solar irradiance with varying load, and (iii) varying solar irradiance under constant load conditions. A dual loop proportional-integral controller-based strategy was implemented to regulate the converters, maintaining DC-link voltage and grid frequency. The simulation results show that the system demonstrates effective adaptation to sudden changes in various conditions, maintaining power quality. The results show that the proposed bipolar microgrid architecture helps to coordinate the system to maintain stable DC-link voltage and achieve balanced power distribution, and ensure smooth battery charging and discharging under dynamic operating conditions.

Performance Evaluation of a Grid-Connected Single-Phase PWM Inverter for Solar Photovoltaic Systems

The increasing integration of renewable energy sources has increased the demand for efficient power electronic converters for grid-connected applications. Among renewable technologies, solar photovoltaic (PV) systems are widely adopted due to their clean energy generation and modular design. Since PV arrays produce direct current (DC), a high-performance inverter is required to supply alternating current (AC) power to loads and the utility grid. This paper presents the design, mathematical modeling, and simulation of a single-phase Pulse Width Modulation (PWM) inverter for solar PV applications using MATLAB/Simulink. The proposed system consists of a PV array, Maximum Power Point Tracking (MPPT) controller, H-bridge SPWM inverter, LCL filter, transformer, and grid interface. The MPPT controller maximizes PV power extraction, while the SPWM technique ensures stable switching with low harmonic distortion. Simulation results under different operating conditions confirm that the proposed inverter delivers a stable sinusoidal AC output with good voltage regulation and low Total Harmonic Distortion (THD). The LCL filter effectively suppresses switching harmonics, improving power quality and grid compatibility. The developed model provides a reliable platform for future research on advanced control strategies and smart grid integration.