A Novel Single Phase Grid Connected Transformer-Less Solar Micro-Inverter Topology With Power Decoupling Capability

Paul, Arup Ratan; Bhattacharya, Arghyadip; Chatterjee, Kishore

doi:10.1109/tia.2022.3207445

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…Paul et al proposed a new transformerless microinverter topology. Simulation and experimental studies verified the effectiveness and feasibility of the scheme (Paul et al 2023). For transmission system parameter identification, Xia et al devised a novel multi-scale folded attention map convolutional network to get over the drawbacks of conventional deep learning techniques.…”

Section: Related Workmentioning

confidence: 72%

Automatic grid topology detection method based on Lasso algorithm and t-SNE algorithm

Huang,

Que,

Zhang

et al. 2024

Energy Inform

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For a long time, the low-voltage distribution network has the problems of untimely management and complex and frequently changing lines, which makes the problem of missing grid topology information increasingly serious. This study proposes an automatic grid topology detection model based on lasso algorithm and t-distributed random neighbor embedding algorithm. The model identifies the household-variable relationship through the lasso algorithm, and then identifies the grid topology of the station area through the t-distributed random neighbor embedding algorithm model. The experimental results indicated that the lasso algorithm, the constant least squares algorithm and the ridge regression algorithm had accuracies of 0.88, 0.80, and 0.71 and loss function values of 0.14, 0.20, and 0.25 for dataset sizes up to 500. Comparing the time spent on identifying household changes in different regions, in Region 1, the training time for the Lasso algorithm, the Constant Least Squares algorithm, and the Ridge Regression algorithm is 2.8 s, 3.0 s, and 3.1 s, respectively. The training time in region 2 is 2.4s, 3.6s, and 3.4s, respectively. The training time in region 3 is 7.7 s, 1.9 s, and 2.8 s, respectively. The training time in region 4 is 3.1 s, 3.6 s, and 3.3 s, respectively. The findings demonstrate that the suggested algorithmic model performs better than the other and can identify the structure of LV distribution networks.

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Section: Related Workmentioning

confidence: 72%

Automatic grid topology detection method based on Lasso algorithm and t-SNE algorithm

Huang,

Que,

Zhang

et al. 2024

Energy Inform

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An integrated common ground‐based grid‐connected current‐fed switched inverter

Ravitheja,

Sahoo,

Thirumala

2024

Circuit Theory & Apps

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A current‐fed switched inverter and its derivatives are gaining more attention in solar PV grid‐connected applications. In these inverters, the absence of galvanic isolation generates the leakage current, leading to the injection of harmonics and the degradation of solar PV panel life span. This paper proposes an integrated common ground‐based grid‐connected current‐fed switched inverter. The common ground between the input DC source and output AC grid eliminates the leakage current and makes the inverter immune to high‐frequency common‐mode voltage variations. It utilizes the shoot‐through state to boost the input DC voltage and obtain the rated AC output voltage. Moreover, as compared to the contemporary common ground‐based inverter topologies, the number of passive reactive elements and active switches is less. Thereby, the size, cost of the system, and associated power loss are reduced. The proposed inverter's continuous input current and buck–boost nature make it suitable for applications like solar PV systems. The steady‐state analysis of the proposed inverter is validated using the MATLAB Simulink tool, and furthermore, the simulation results during grid‐tied mode have been cross‐verified with the help of real‐time simulator OPAL‐RT. Further, the power loss and thermal loss analysis are carried out using the PLECS software. Finally, the performance of the inverter and simulation results are verified with the experimental results.

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Comparative Analysis of Three Phase Hybrid Transformerless Inverters for PV applications

Kumar,

Ramasamy,

Losito

et al. 2023

2023 International Conference on Clean Electrical Power (ICCEP)

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A Novel Single Phase Grid Connected Transformer-Less Solar Micro-Inverter Topology With Power Decoupling Capability

Cited by 6 publications

References 24 publications

Automatic grid topology detection method based on Lasso algorithm and t-SNE algorithm

Automatic grid topology detection method based on Lasso algorithm and t-SNE algorithm

An integrated common ground‐based grid‐connected current‐fed switched inverter

Comparative Analysis of Three Phase Hybrid Transformerless Inverters for PV applications

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