Abstract

This paper presents a frequency adaptive grid voltage sensorless control scheme of a grid-connected inductive–capacitive–inductive (LCL)-filtered inverter, which is based on an adaptive current controller and a grid voltage observer. The frequency adaptive current controller is constructed by a full-state feedback regulator with the augmentation of multiple control terms to restrain not only the inherent resonance phenomenon that is caused by LCL filter, but also current harmonic distortions from an adverse grid environment. The number of required sensing devices is minimized in the proposed scheme by means of a discrete-time current-type observer, which estimates the system state variables, and gradient-method-based observers, which estimate the grid voltages and frequency simultaneously at different grid conditions. The estimated grid frequency is utilized in the current control loop to provide high-quality grid-injected currents, even under harmonic distortions and the frequency variation of grid voltages. As a result, the grid frequency adaptive control performance as well as the robustness against distorted grid voltages can be realized. Finally, an inverter synchronization task without using grid voltage sensors is accomplished by a fundamental grid voltage filter and a phase-locked loop to detect the actual grid phase angle. The stability and convergence performance of the proposed observers have been studied by means of the Lyapunov theory to ensure a high accuracy tracking performance of estimated variables. Simulation and experimental results are presented to validate the feasibility and the effectiveness of the proposed control approach.

Highlights

  • The distributed generation (DG) employing renewable energy from wind or solar sources has gained a lot of interest from energy producers and consumers, owing to environment-friendly features [1]

  • The control performance of a grid-connected inverter should be robust against the negative effects from the main grid, such as the harmonic distortion or the grid frequency deviation, to inject the active power from DG to the grid with high-quality injected currents, which complies with the restricted grid codes [3]

  • For the performance evaluation of the proposed voltage sensorless current control scheme, simulations have been carried out for an LCL-filtered grid-connected inverter that is based on the PSIM software under different operating conditions

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Summary

Introduction

The distributed generation (DG) employing renewable energy from wind or solar sources has gained a lot of interest from energy producers and consumers, owing to environment-friendly features [1]. These schemes require quite heavy computational burden since all the harmonic components in the distorted grid condition should be included into system model in order to ensure good convergence of the estimated voltages to the actual quantities Another approach in [19] presents a model reference adaptive control structure by using the active- and reactive-power model to estimate the grid voltages. The uncertainty in the main grid, such as the grid frequency variation and harmonic distortion, poses a challenge for the synchronization task of the voltage sensorless control scheme To overcome this issue, a resonant filter in the discrete-time state-space tuned at the fundamental frequency cooperated with the conventional PLL scheme to restrain the effect of the harmonic distortion on the extracted grid phase angle. The proposed current controller has robustness against negative impacts from grid, such as the harmonic distortion and the grid frequency variation, even without grid voltage sensors

System Description
Frequency Adaptive Current Controller
Discrete-Time Current-Type Full State Observer
Adaptive Gradient Steepest Descent Method for Grid Voltage Estimation
Adaptive Filter to Extract the Grid Fundamental Component
Grid Frequency Estimation Based on an Adaptive Least-Mean-Square Algorithm
Simulation Results
Simulation results responsesunder under distorted grid voltage the proposed
10. Simulation
11. Simulation
12. Simulation
Experimental Results
Experimental
15. Experimental
16. It isthat observed that the grid injected are considerably grid condition
17. Experimental
A to 6response
22. Comparative results of the phase angle estimation between proposed control
23. Comparative
24. Experimental results undergrid grid frequency variation
Conclusions

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