Transformer Less Grid Feeding Current Source Inverter for Solar Photovoltaic System

Abstract

With increasing amounts of solar power being attached to the electrical grid, there has been a rush in developing improved, smaller power electronics capable of dealing with the task effectively. In the traditional grid-tied solar systems, individuals typically add large line-frequency transformers to maintain the electrical isolation of the PV panels. This arrangement is safe, but with disadvantages, including additional loss of energy, reduction of size and weight, and increased costs of construction and equipment. That is why the idea to design and simulate a transformer less grid-feeding current source inverter (CSI) in the specific case of solar PV systems has become quite popular. The aim is to make a smooth changeover between the solar panels and the utility grid and maintain good power quality and low harmonics. An Incremental Conductance MPPT algorithm is used to extract as much power as possible out of the PV array under varying conditions (such as sunlight variations). This algorithm is used with a DC-DC boost converter that varies the operating point of the PV, and injects the appropriate current into the inverter stage to maintain the current sent to the grid clean, and follow the reference (that is, under different conditions, etc.). Two methods are compared to control the current injected into the grid: A simple proportional-integral (PI) controller as the reference point. An Artificial Neural Network (ANN)-based smart controller as the control attempting to handle the nonlinear behaviour and adapt to new conditions and respond more quickly and correctly to new conditions and situations. The results of the simulations indicate that the ANN controller could provide much better results: reduced harmonic distortion of the grid current in comparison with the traditional PI approach; moreover, the solution remains easily within the IEEE harmonic limits and provides a more efficient, smaller scale solution to current solar PV grid integration.

Introduction

The increasing global energy demand and environmental concerns have accelerated the adoption of renewable energy, with solar photovoltaic (PV) systems emerging as a popular solution due to their modularity, ease of installation, and decreasing costs. Grid-connected PV systems allow direct injection of solar power into the utility grid but require efficient power electronic converters to convert DC from PV panels into AC compatible with grid standards.

Traditional transformer-based inverters provide electrical isolation but increase losses, size, and cost, while transformer-less inverters offer higher efficiency, compactness, and lower costs. However, they introduce challenges such as leakage current and harmonics, requiring careful inverter design and control strategies.

Conventional controllers (PI, PR) often underperform under nonlinear conditions or varying environmental factors. To overcome these limitations, Artificial Neural Network (ANN)-based controllers are used for transformer-less Current Source Inverters (CSI). ANN control provides:

• Reduced grid current harmonic distortion (THD)
• Faster transient response
• Robust performance under parameter variations and changing irradiance

The proposed system integrates a PV array, DC–DC boost converter with MPPT (Maximum Power Point Tracking), and transformer-less CSI to efficiently feed power into the grid. The DC–DC converter optimizes PV output voltage using the Incremental Conductance MPPT algorithm, ensuring maximum power extraction under dynamic irradiance and temperature conditions. The CSI converts regulated DC to AC, and a grid interface filter ensures high-quality power delivery.

This approach addresses key challenges in grid-connected PV systems, improving efficiency, reliability, and power quality while leveraging intelligent control to enhance performance beyond conventional methods.

Conclusion

This thesis is an in-depth design and simulation study of a transformerless grid connected, current-source inverter (CSI) adapted to be used in solar photovoltaic (PV) applications. The overall goal of the task was to design an effective system of power conversion that can achieve the maximum power output out of the PV array and provide the output power to the utility grid with an acceptable power quality. The proposed structure incorporates a PV array, a DC-DC boost converter with an Incremental Conductance based maximum power point tracking (MPPT) algorithm, a DC-link current source stage, and a CSI to enable an easy access to the grid. The boost converter controls the operating point of the PV array and puts the system under operating conditions that are within the vicinity of the maximum power point even with changing conditions in solar irradiance. DC-link inductor maintains a constant current source on the inverter input and in this way, simplifies the current regulation and makes the system very reliable. There were two different control strategies introduced to control the inverter output current. The first step involved the use of a traditional proportional integral (PI) controller with the aim of creating a reference performance benchmark. Although the PI controller managed to hold the basic current regulation in steady state conditions, its operation under dynamic variation in operating parameters was worse. In order to overcome these limitations, a controller using artificial neural network (ANN) was proposed. ANN controller was developed to adjust to the nonlinear nature of system hence enhancing grid current regulation. The findings of the simulation carried out with the help of the MATLAB/Simulink were that in the cases when the ANN-based control method was applied, the current waveforms were smoother and less harmonic distortion was present compared to the standard PI controller. These results further supported the fact that the proposed system is effective in injecting current into the grid and ensuring that it is synchronized with the grid voltage. Analysis of harmonic showed that grid-current distortion was within the acceptable maximums of the IEEE-519 standard. The overall results prove that the transformer-less CSI topology, in combination with smart control, is able to significantly enhance the output of PV systems connected to the grid. On the balance, the paper has testified to the feasibility of the suggested system as a trusted method of solar photovoltaic generation into the electric grid, and at the same time maintain quality power and optimal energy conversion. The significant findings of this study can be formulated as follows: 1) Construction of a transformer-free grid-connected photovoltaic power conversion system based on a current-source inverter topology. 2) Application of the Incremental Conductance MPPT algorithm to ensure the extracting solar energy is efficient in different environmental conditions. 3) To ensure a constant current input to the inverter a DC-link current source stage was designed and simulated. 4) Implementation and evaluation of a conventional PI controller for inverter current regulation. 5) Development of an ANN-based intelligent controller to improve dynamic performance and reduce harmonic distortion. 6) Comparative analysis of PI and ANN control strategies based on simulation results and harmonic performance. 7) Verification that the proposed system satisfies IEEE-519 harmonic standards for grid-connected operation.

References

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Copyright

Copyright © 2026 Boini Venkatesh, K. V. Sharma, M. T. Naik. 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.