Dual buck grid-connected inverter based on GaN devices

2016 ◽  
Author(s):  
Lu Wang ◽  
Yan Li ◽  
Qi Yan ◽  
Wei Dou
Keyword(s):  
Grid Connected Inverter

Phase-Locked Loop Using Complex-Coefficient Filters for Grid-Connected Inverter

2013 ◽  
Vol 133 (4) ◽  
pp. 388-394 ◽  
Author(s):  
Akihiro Ohori ◽  
Nobuyuki Hattori ◽  
Tsuyoshi Funaki
Keyword(s):  
Phase Locked Loop ◽  
Complex Coefficient ◽  
Grid Connected Inverter

DC to Single-Phase AC Grid Connected Inverter with Boost Type Active Buffer Circuit Operated in Discontinuous Current Mode

2018 ◽  
Vol 138 (5) ◽  
pp. 453-462
Author(s):  
Jun-ichi Itoh ◽  
Tomokazu Sakuraba ◽  
Hoai Nam Le ◽  
Hiroki Watanabe ◽  
Keisuke Kusaka
Keyword(s):  
Single Phase ◽  
Current Mode ◽  
Grid Connected Inverter

Model predictive flexible power control for grid-connected inverter under unbalanced network

2020 ◽  
Vol 13 (16) ◽  
pp. 3580-3589 ◽  
Author(s):  
Bihua Hu ◽  
Zhiyong Chen ◽  
Zhi Zhang ◽  
Siyan Liu ◽  
Wenlang Deng
Keyword(s):  
Power Control ◽  
Grid Connected Inverter ◽  
Unbalanced Network

Voltage Sensorless Model Predictive Control for a Grid-Connected Inverter with LCL Filter

2021 ◽  
pp. 1-1
Author(s):  
Nguyen Ngoc Ngoc Nam ◽  
Ngoc-Duc Nguyen ◽  
Changwoo Yoon ◽  
Minho Choi ◽  
Young Il Lee
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Lcl Filter ◽  
Grid Connected Inverter

scholarly journals An Optimized PV Control System Based on the Emperor Penguin Optimizer

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 751
Author(s):  
Mariam A. Sameh ◽  
Mostafa I. Marei ◽  
M. A. Badr ◽  
Mahmoud A. Attia
Keyword(s):  
Duty Cycle ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Boost Converter ◽  
Emperor Penguin ◽  
Pv System ◽  
Partial Shading ◽  
Pv Systems ◽  
Point Tracking ◽  
Grid Connected Inverter

During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.

scholarly journals Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 942
Author(s):  
Myada Shadoul ◽  
Hassan Yousef ◽  
Rashid Al Abri ◽  
Amer Al-Hinai
Keyword(s):  
Adaptive Fuzzy Control ◽  
System Stability ◽  
Adaptive Fuzzy ◽  
Fuzzy Approximation ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Modelling Uncertainties ◽  
Grid Connected Inverter ◽  
Takagi Sugeno

Three-phase inverters are widely used in grid-connected renewable energy systems. This paper presents a new control methodology for grid-connected inverters using an adaptive fuzzy control (AFC) technique. The implementation of the proposed controller does not need prior knowledge of the system mathematical model. The capabilities of the fuzzy system in approximating the nonlinear functions of the grid-connected inverter system are exploited to design the controller. The proposed controller is capable to achieve the control objectives in the presence of both parametric and modelling uncertainties. The control objectives are to regulate the grid power factor and the dc output voltage of the photovoltaic systems. The closed-loop system stability and the updating laws of the controller parameters are determined via Lyapunov analysis. The proposed controller is simulated under different system disturbances, parameters, and modelling uncertainties to validate the effectiveness of the designed controller. For evaluation, the proposed controller is compared with conventional proportional-integral (PI) controller and Takagi–Sugeno–Kang-type probabilistic fuzzy neural network controller (TSKPFNN). The results demonstrated that the proposed AFC showed better performance in terms of response and reduced fluctuations compared to conventional PI controllers and TSKPFNN controllers.

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